Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Parallel application”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗
DOE OSTI2021

Battle of the Defaults: Extracting Performance Characteristics of HDF5 under Production Load

Popular parallel I/O libraries, such as HDF5, provide tuning parameters to obtain superior performance. However, the selection of effective parameters on production systems is complex due to the interdependence of I/O software and file system layers. Hence, application developers typically use the default parameters and often experience poor I/O performance. This work conducts a benchmarking-based analysis on the HDF5 behaviors with a wide variety of I/O patterns to extract performance characteristics under the production workload. To make the analysis well controlled, we exercise I/O benchmarks on POSIX-IO, MPI-IO, and HDF5 using the same I/O patterns and in the same jobs. To address high performance variability in production environments, we repeat the benchmarks across I/O patterns, storage devices, and time intervals. Based on the results, we identified consistent HDF5 behaviors that appropriate configurations and operations on dataset layout and file-metadata placement can improve performance significantly. We apply our findings and evaluate the tuned I/O library on two supercomputers: Summit and Cori. The results show that our tuned parameters can achieve more than 10× I/O performance speedup than that with default parameters on both systems, suggesting the effectiveness, stability, and generality of our solution.

Xie, Bing↗
DOE OSTI2022

Demand Driven Energy Management for PIPO Auxiliary Power Supply Architecture

In this paper, a demand driven energy management (DDEM) technique is implemented on modular parallel input parallel output DC/DC auxiliary power supply (APS) structure. In this method, modules are brought in/out of the operation making them operate under high loading conditions, avoiding low efficiency under light load. Such a method finds its application in power conservative systems with variable load. Implementation of DDEM and the energy saved is shown using APS comprised of two 15W commercially off-the-shelf DC-DC modules, with 100–1000 VDC input and 24 V output voltage. Along with the DDEM, the lifetime of the power stage devices is controlled minimizing their junction temperature swing through control of cooling system. This control method uses digital twin as interface to virtualize the power devices loading operation/ health condition. Accordingly, no extra sensor or closed loop control required to measure the junction temperature of the devices or feedback the losses to the cooling flow.

Azadeh, Yalda↗
DOE OSTI2021

ACCELERATOR CHALLENGES OF THE LHeC PROJECT

The LHeC project studies the design of a future electron-proton collider at CERN that will run in parallel to the standard LHC operation. For this purpose, the existing LHC storage ring will be combined with an Energy Recovery Linac (ERL), to accelerate electrons up to kinetic energy of 50 GeV. This concept - also applicable to the FCC-eh collider and studied at the PERLE project as prototype version - allows a peak luminosity of 10³⁴ cm⁻² s⁻¹. A sophisticated design of the RF structures, linacs, arcs, and interaction region is required. The electrons are accelerated and, after the interaction point, their energy is recovered through the same RF structures. While this energy recovery concept is a very promising approach, severe challenges are set by the layout of the interaction region, the beam separation concept and the design of the linac and arc lattice for the highest possible momentum acceptance. Emittance control and beam-beam effect of both, electron and proton beams, have been studied in front-to-end simulations and will be presented. We summarise the design principles of the ERL, the optimization of the arc lattice, and the main parameters of the project.

Holzer, B. J.↗
DOE OSTI2020

Separate Effects Validation for Subcooled Boiling in CTF

In 2010, the U.S. Department of Energy created its first Energy Innovation Hub, which is focused on developing high-fidelity and high-resolution modeling and simulation (M&S) tools for modeling of light water reactors (LWRs). This hub, the Consortium for Advanced Simulation of LWRs (CASL), has developed an LWR simulation tool called the Virtual Environment for Reactor Applications (VERA). The multi-physics capability of VERA is achieved through the coupling of single-physics codes, including CTF (the CASL version of Coolant Boiling in Rod Arrays— Three Field (COBRA-TF)), Michigan Parallel Characteristics Transport (MPACT), BISON, and Materials Performance and Optimization (MPO) Advanced Model for Boron Analysis (MAMBA). As part of its M&S efforts, CASL has identified various challenge problems, including Crud Induced Power Shift (CIPS), Crud-Induced Localized Corrosion (CILC), Pellet-Cladding Interaction (PCI), and Departure from Nucleate Boiling (DNB). This work addresses CASL milestone L2:VVI.P19.03, which focuses on uncertainty quantification of crud, which is relevant to both CIPS and CILC. This is achieved through an analysis and separate effects validation of the thermal hydraulic phenomenon known as subcooled boiling. As part of this work, various sources of experimental data are examined and compared to different options for empirical modeling of subcooled boiling. Through this analysis, a complete understanding of the underlying models and their implementation details are understood. A subset of these data are incorporated into a separate effects validation study of CTF. The Westinghouse Advanced Loop Tester (WALT) and Rohsenow experiments are modeled, and it is shown that the newly-implemented Gorenflo correlation is more accurate than the existing Chen and Thom correlations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be maximized with portable programming models that are able to maintain high performance across architectures. While in many cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higher-level directive-based abstractions. MiniMDock is a new mini-application (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in phar-maceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA and HIP on NVIDIA and AMD GPUs, respectively, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.

Thavappiragasam, Mathialakan↗
DOE OSTI2023

The Kokkos OpenMPTarget Backend: Implementation and Lessons Learned

As the supercomputing landscape diversifies, solutions such as Kokkos to write vendor agnostic applications and libraries have risen in popularity. Kokkos provides a programming model designed for performance portability, which allows developers to write a single source implementation that can run efficiently on various architectures. At its heart, Kokkos maps parallel algorithms to architecture and vendor specific backends written in lower level programming models such as CUDA and HIP. Another approach to writing vendor agnostic parallel code is using OpenMP’s directives based approach, which lets developers annotate code to express parallelism. It is implemented at the compiler level and is supported by all major high performance computing vendors, as well as the primary Open Source toolchains GNU and LLVM. Since its inception, Kokkos has used OpenMP to parallelize on CPU architectures. In this paper, we explore leveraging OpenMP for a GPU backend and discuss the challenges we encountered when mapping the Kokkos APIs and semantics to OpenMP target constructs. As an exemplar workload we chose a simple conjugate gradient solver for sparse matrices. We find that performance on NVIDIA and AMD GPUs varies widely based on details of the implementation strategy and the chosen compiler. Furthermore, the performance of the OpenMP implementations decreases with increasing complexity of the investigated algorithms.

Gayatri, Rahulkumar↗
DOE OSTI2021

Time-periodic steady-state solution of fluid-structure interaction and cardiac flow problems through multigrid-reduction-in-time

In this study, a time-periodic MGRIT algorithm is proposed as a means to reduce the time-to-solution of numerical algorithms by exploiting the time periodicity inherent to many applications in science and engineering. The time-periodic MGRIT algorithm is applied to a variety of linear and nonlinear single- and multiphysics problems that are periodic-in-time. It is demonstrated that the proposed parallel-in-time algorithm can obtain the same time-periodic steady-state solution as sequential time-stepping. It is shown that the required number of MGRIT iterations can be estimated a priori and that the new MGRIT variant can significantly and consistently reduce the time-to-solution compared to sequential time-stepping, irrespective of the number of dimensions, linear or nonlinear PDE models, single-physics or coupled problems and the employed computing resources. The numerical experiments demonstrate that the time-periodic MGRIT algorithm enables a greater level of parallelism yielding faster turnaround, and thus, facilitating more complex and more realistic problems to be solved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Failure analysis of nuclear transient-tested UN tristructural isotropic fuel particles in a 3D printed SiC matrix

Fully ceramic microencapsulated fuel elements containing UN tristructural isotropic (TRISO) fuel particles within a 3D printed SiC matrix were subjected to transient testing with varying energy depositions. Detailed post-irradiation examinations were performed, including leaching in hot HNO3 and post-leaching X-ray computed tomography, to quantify the percentage of failed TRISO particles and crack propagation within the particles and surrounding fuel matrix. In parallel, detailed finite element analyses were performed for comparison with experimental findings and to better evaluate transient failure modes. The lowest transient energy deposition—which still exceeded bounding values for high-temperature gas-cooled reactor applications—resulted in no detectable TRISO particle failures or matrix cracking, which was consistent with the simulations. Simulations of the higher-energy transients for which significant TRISO particle failure was expected were generally able to reproduce the transient temperatures and matrix cracking. Thus, the TRISO particle failures were explained based on the effects of local SiC matrix thickness and porosity. Results generally confirmed the high strength of the additively manufactured SiC matrix but also affirmed the need for a modified UN TRISO architecture to prevent SiC matrix cracks from propagating through TRISO layers. This unique failure mode has not historically been considered for TRISO fuels contained in weaker graphite matrices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops

Chemical transformations on catalyst surfaces occur through series and parallel reaction pathways. These complex networks and their behavior can be most simply evaluated through a three-species surface reaction loop (A* to B* to C* to A*) that is internal to the overall chemical reaction. Application of an oscillating dynamic catalyst to this reactive loop has been shown to exhibit one of three types of behavior: (1) a positive net flux of molecules about the loop in the clockwise direction, (2) a negative net flux of molecules about the loop in the counterclockwise direction, or (3) negligible flux of molecules about the loop at the limit cycle of reaction. Three-species surface loops were simulated with microkinetic modeling to assess the reaction loop behavior resulting from a catalytic surface oscillating between two or more catalyst surface energy states. Selected input parameters for the simulations spanned an 11-dimensional parameter space using 127 688 different parameter combinations. Their converged limit cycle solutions were analyzed for their loop turnover frequencies, the majority of which were found to be approximately zero. Classification and regression machine learning models were trained to predict the sign and magnitude of the loop turnover frequency and successfully performed above accessible baselines. Notably, the classification models exhibited a baseline weighted F1 score of 0.49, whereas trained models achieved weighted F1 scores of 0.94 and 0.96 when trained on the parameters used to define the simulations and derived rate constants, respectively. The trained models successfully predicted catalytic loop behavior, and interpretation of these models revealed all input parameters to be important for the prediction and performance of each model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Substantial reduction of the anisotropy in the critical current densities J c of Ni-doped CaKFe 4 As 4 single crystals by chemical and irradiation-induced disorder

Here, we demonstrate that the anisotropy in the critical current densities, J c , of CaK(Fe 1–x Ni x ) 4 As 4 (x = 0, 0.015, 0.025, and 0.030) single crystals reduces with increasing chemical and irradiation-induced disorder. The magnetic field dependences of J c are analyzed by performing magnetization measurements with H applied parallel and perpendicular to the crystallographic c-axis. The results show that undoped crystals display large anisotropies in Jc due to an enhancement of the vortex pinning with H applied parallel to the crystallographic ab-planes. This anisotropy reduces substantially as Ni addition increases. Moreover, we found that random disorder introduced by proton irradiation enhances mainly the vortex pinning for H parallel to the c-axis. Consequently, using adequate fluencies, the vortex pinning at low temperatures in both undoped and doped samples becomes close to isotropic. These results make the CaKFe 4 As 4 system promising for applications that require isotropic J c under magnetic fields.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Revealing Phase Heterogeneity in Vertically Aligned Nanocomposites via Plan-View Electron Energy Loss Spectroscopy

Hydrogen utilization in clean energy technologies is challenged by limited storage and transport within materials, owing to the complex hydrogen kinetics at interfaces [1]. Understanding these interfacial mechanisms at the nanoscale is crucial for developing improved materials for hydrogen applications, particularly proton-conducting fuel cells (PCFCs). Vertically aligned nanocomposites (VANs) grown by pulsed laser deposition (PLD) offer a unique platform for investigating the interfacial effects on hydrogen transport due to their well-defined interfaces parallel to the direction of charge transport [2-4]. To investigate hydrogen transport, the two phases within the VANs were chosen as BaZr 0.9 Y 0.1 O 3-x (BZY), a known proton conductor, and Pr 0.1 Ce 0.9 O 2-x (PCO), a mixed ionic-electronic conductor [5]. This PCO-BZY VANs architecture allows the investigation of how the interface between a proton conductor and a mixed conductor influences hydrogen transport. However, because of the small size of hydrogen, it is difficult to discern the nature of its interactions with interfaces from bulk measurements at the macroscale, thus necessitating nanoscale measurements [6]. Electron energy loss spectroscopy (EELS) allows for nanometer-resolution probing of the local atomic structure and chemistry at the BZY/PCO interface. In this study, plan-view analysis of PCO-BZY VANs films was employed to characterize the structure and phase distribution of the VANs and investigate the interface between the nanostructures. The films were imaged using scanning electron microscopy (SEM) in the Hitachi S-4800 SEM, collecting secondary electron images using mixed upper and lower detectors. Then, plan-view transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) EELS were employed using a JEOL ARM300 microscope operated at 300kV with a Gatan K3 GIF Continuum detector to study the distribution of the BZY and PCO phases through the film. As a result, spectrum images were acquired at a dispersion of 0.18eV per channel and denoised afterward by principal component analysis (PCA) method.

Griffin, Elizabeth [Northwestern University, Evans↗
DOE OSTI2020

Containers for Massive Ensemble of I/O Bound Hierarchical Coupled Simulations

We present our experience using containers to scale up a massive ensemble of coupled I/O bound workloads on the NERSC Cori supercomputer. We describe the design of a hierarchical simulation structure using the Integrated Plasma Simulator (IPS) that enables the flexible execution of coupled simulations at the system, node, and core level using the same coupling abstraction and API. The hierarchical design allows for the node-level execution to be efficiently executed using containers while not impacting the structure of the simulation at the system level. We demonstrate the viability of the approach by presenting experimental results from applications in coupled fusion plasma simulations that illustrate the performance impact of using containers to deploy the node-level workloads, in conjunction with the user mountable XFS file systems to ameliorate the load on the Lustre parallel file system. We also present results from production runs showing the ability of the ensemble simulations to scale to hundreds of Cori Haswell nodes, with little or no overhead.

Elwasif, Wael↗
DOE OSTI2020

Auxin‐induced actin cytoskeleton rearrangements require AUX1

Summary The actin cytoskeleton is required for cell expansion and implicated in cellular responses to the phytohormone auxin. However, the mechanisms that coordinate auxin signaling, cytoskeletal remodeling and cell expansion are poorly understood. Previous studies examined long‐term actin cytoskeleton responses to auxin, but plants respond to auxin within minutes. Before this work, an extracellular auxin receptor – rather than the auxin transporter AUXIN RESISTANT 1 (AUX1) – was considered to precede auxin‐induced cytoskeleton reorganization. In order to correlate actin array organization and dynamics with degree of cell expansion, quantitative imaging tools established baseline actin organization and illuminated individual filament behaviors in root epidermal cells under control conditions and after indole‐3‐acetic acid (IAA) application. We evaluated aux1 mutant actin organization responses to IAA and the membrane‐permeable auxin 1‐naphthylacetic acid (NAA). Cell length predicted actin organization and dynamics in control roots; short‐term IAA treatments stimulated denser and more parallel, longitudinal arrays by inducing filament unbundling within minutes. Although AUX1 is necessary for full actin rearrangements in response to auxin, cytoplasmic auxin (i.e. NAA) stimulated a lesser response. Actin filaments became more ‘organized’ after IAA stopped elongation, refuting the hypothesis that ‘more organized’ actin arrays universally correlate with rapid growth. Short‐term actin cytoskeleton response to auxin requires AUX1 and/or cytoplasmic auxin.

AUX1↗
DOE OSTI2025

1D model of SOL with self consistent calculation of non-coronalradiation and applications to shallow field line angles

A 1D model of the scrape off layer (SOL) is created with a physics based calculation of the impurity radiation and the inclusion of the perpendicular heat flux q ⊥ compared to previous 1D models. The calculation of the impurity radiation includes the addition of a model using parallel and perpendicular motion to calculate the impurity confinement time τ and the inclusion of charge exchange between neutral hydrogen and impurity ions. Reasonable agreement is found between the model and the 2D fluid code SOLPS-ITER. Additionally, an improvement is shown over the constant τ used in previous works The model is used to examine the effects of shallow field line angles at ITER levels of parallel heat flux (q || ). A large increase in radiation results in an increase in the upstream parallel heat flux which can still be in detachment at shallow field line angles for both q ⊥ and the calculated τ for traditional impurities (argon and nitrogen) as well as boron. The physics behind this increase in radiation at shallow field line angles is shown to be a decrease in τ and a change in the temperature profile with the inclusion of q ⊥ .

ITER↗