An enterprise control assessment case study of the energy–water nexus for the ISO New England system
Not Available
Muhanji, Steffi Olesi↗
SEARCH · Engineering Papers
Results for “casing integrity”
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.
Path integral approach to quantum anomalies in interacting models
The prediction and subsequent discovery of topological semimetal phases of matter in solid state systems has instigated a surge of activity investigating the exotic properties of these unusual materials. Among these are transport signatures which can be attributed to the chiral anomaly; the breaking of classical chiral symmetry in a quantum theory. This remarkable quantum phenomenon, first discovered in the context of particle physics has now found new life in condensed-matter physics, connecting topological quantum matter and band theory with effective field theoretic models. In this paper we investigate the interplay between interactions and the chiral anomaly in field theories inspired by semimetals using Fujikawa's path integral method. Starting from models in one spatial dimension we discuss how the presence of interactions can affect the consequences of the chiral anomaly leading to renormalization of excitations and their transport properties. This is then generalized to the three-dimensional case where we show that the anomalous response of the system, namely, the chiral magnetic and quantum Hall effects, are modified by the presence of interactions. These properties are investigated further through the identification of anomalous modes which exist within interacting Weyl semimetals. These massive excitations are nonperturbative in nature and are a direct consequence of the chiral anomaly. The effects of interactions on mixed axial-gravitational anomalies are then investigated and the conditions required for interaction effects to be observed are discussed.
Anomalies↗
Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem
We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.
Hahn, Steven↗
$T\overline{T}$ deformations and the width of fundamental particles
We provide a simple geometric meaning for deformations of so-called $T\overline{T}$ type in relativistic and non-relativistic systems. Deformations by the cross products of energy and momentum currents in integrable quantum field theories are known to modify the thermodynamic Bethe ansatz equations by a “CDD factor”. In turn, CDD factors may be interpreted as additional, fixed shifts incurred in scattering processes: a finite width added to the fundamental particles (or, if negative, to the free space between them). We suggest that this physical effect is a universal way of understanding $T\overline{T}$ deformations, both in classical and quantum systems. We first show this in non-relativistic systems, with particle conservation and translation invariance, using the deformation formed out of the densities and currents of particles and momentum. This holds at the level of the equations of motion, and for any interaction potential, integrable or not. We then argue, and show by similar techniques in free relativistic particle systems, that $T\overline{T}$ deformations of relativistic systems produce the equivalent phenomenon, accounting for length contractions. We also show that, in both the relativistic and non-relativistic cases, the width of particles is equivalent to a state-dependent change of metric, where the distance function discounts the particles’ widths, or counts the additional free space. This generalises and explains the known field-dependent coordinate change describing $T\overline{T}$ deformations. The results connect such deformations with generalised hydrodynamics, where the relations between scattering shifts, widths of particles and state-dependent changes of metric have been established.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management
Growing shale gas extraction in recent years has triggered wide discussions on the associated freshwater requirement and wastewater management. Many optimization approaches have been developed for shale gas water management; however, most of the studies assumed permanent utilization of wastewater treatment facilities with fixed capacities. Considering the rapidly declining characteristics of shale gas wastewater production, these treatment facilities could remain largely underutilized after the first few months/years of production, making them less economically attractive. To maximize the capacity utilization of treatment facilities and further improve the economic performance of shale gas development, this study develops a systematic optimization framework, where the capacity strategy of conventional treatment facilities and utilization of the recent concept of modular manufacturing are both considered for flexible shale gas water management. The proposed mixed-integer linear programming (MILP) model simultaneously optimizes the design and planning of integrated shale gas and water supply chain, with a focus on capacity planning for both large-scale conventional treatment facilities and small-scale modular devices. A series of Marcellus-based case studies are performed to illustrate the applicability of the proposed model and provide general insights into the trade-offs between the multiple types of treatment facilities. The optimization results reveal that the combinatorial utilization of conventional facilities and modular devices for wastewater treatment (66% by conventional facilities and 34% by modular devices) brings 9.3% more reused water for other well development and 6.2% savings in water-related costs, compared to flexible management of only conventional facilities. Furthermore, this work suggests that taking modular device as auxiliary equipment for shale gas water management is most beneficial to increase the capacity utilization of treatment facilities and achieve a more economic and sustainable shale gas production system.
04 OIL SHALES AND TAR SANDS↗
Multi-mode quasi-static excitation for systems with nonlinear joints
Finite element models can be used to model and predict the hysteresis and energy dissipation exhibited by nonlinear joints in structures. As a result of the nonlinearity, the frequency and damping of a mode is dependent on excitation amplitude, and when the modes remain uncoupled, quasi-static modal analysis has been shown to efficiently predict this behavior. However, in some cases the modes have been observed to couple such that the frequency and damping of one mode is dependent on the amplitude of other modes. To model the interactions between modes, one must integrate the dynamic equations in time, which is several orders of magnitude more expensive than quasi-static analysis. This work explores an alternative where quasi-static forces are applied in the shapes of two or more modes of vibration simultaneously, and the resulting load–displacement curves are used to deduce the effect of other modes on the effective frequency and damping of the mode in question. This methodology is demonstrated on a simple 2D cantilever beam structure with a single bolted joint which exhibits micro-slip nonlinearity over a range of vibration amplitudes. The predicted frequency and damping are compared with those extracted from a few expensive dynamic simulations of the structure, showing that the quasi-static approach produces reasonable albeit highly conservative bounds on the observed dynamics. This framework is also demonstrated on a 3D structure where dynamic simulations are infeasible.
42 ENGINEERING↗
BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty
Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAMLCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAMLCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Symplectic neural network and its application to charged particle dynamics in electromagnetic fields
Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Electric Vehicles, Biofuels, and Transitions in Transportation Energy
The decarbonization of power sources for light duty transportation is moving towards the use of renewable energies, biofuels, and electric vehicles. Currently there are three major light transportation fuels: gasoline (US gasoline is 98% E10), biofuels (E85), and electricity. Currently fuel ethanol in biofuels is obtained principally from corn in the US and sugarcane in Brazil with a small amount from cellulose. The transition to alternative fuels has been occurring since 1980, although light transportation still relies on gasoline. The rise of battery electric vehicles (BEV) is now beginning to offset internal combustion engines (ICE), while recent advances in biotechnology and integrated plant design have enhanced the economic feasibility of cellulosic biofuels. This paper explores the potential role of these fuels in reducing the carbon footprint associated with light transportation. We compare three different cases: Gasoline, E85 and full electric based on the Argonne AFLEET tool to calculate generation of greenhouse gases due to manufacturing and operation of BEV and ICE cars, respectively, in the US assuming an average useful automobile life of 200,000 miles or 12 years. We concluded that significant liquid fuel use (both renewable and non-renewable) will continue for the next 50 years. Here, renewable liquid fuels have an important role to play as the transportation sector transitions from petroleum to other energy sources.
09 BIOMASS FUELS↗
Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations
Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.
17 WIND ENERGY↗
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes
Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.
97 MATHEMATICS AND COMPUTING↗
A Conceptual Design for a Mobile Application to Support Infield Inventory Activities
This paper introduces an inventory assistant being developed at Oak Ridge National Laboratory (ORNL) that we believe will empower users to perform inventory activities at nuclear facilities more accurately, reliably, and quickly. Inventory activities at nuclear facilities are often conducted using pen and paper, which can be time-consuming, tedious, and susceptible to reading or transcription errors. The proposed inventory assistant would replace the paper-based process used by International Atomic Energy Agency (IAEA) inspectors, nuclear facility operators, or verification monitors to complete an inventory of nuclear and non-nuclear items. In general, the inventory assistant would ingest an inventory list, distribute assigned items from the inventory list to one or more mobile devices, enable inventory teams to record their observations in the field, and then enable an inventory lead to integrate and reconcile the observations to produce a final report. The assistant consists of two software components—one for the inventory teams to record observations in the field (In-Field Observations App [IFOA]) and one for the inventory lead to reconcile the inventory list with observations (Distribution, Integration, and Reconciliation Application [DIRA]). This paper introduces the overall workflow of the inventory assistant and describes the IFOA user experience in more detail. To demonstrate the concept, the authors present a use case of IAEA inspectors conducting item counting and tag checking activities of UF6 cylinders at a gas centrifuge enrichment plant with a large number of UF6 cylinders (e.g., thousands). These activities can currently require 30–40 person-days of inspection to complete. Based on experiences during an exercised performed at the IAEA by the ORNL team in 2016, we believe an inventory assistant could allow the IAEA to complete item counting and tag checking using the global identifier or the operator’s barcode in 8–10 person-days of inspection. We would expect other users (e.g., facility operators or verification monitors) to also benefit from significant time savings.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Data Summary Report for Soil and Slab Sampling at Former Building 175
Lawrence Livermore National Security (LLNS) and the U.S. Department of Energy (DOE) are in the process of returning the area where Building 175 existed to beneficial use for future site development. The completed assessment will help LLNS and DOE to outline project scope and costs associated with the removal, characterization, and disposal of materials generated when the concrete slab for the former building and any associated remaining subsurface structures are demolished. Slab removal under the Transition & Disposition process requires full screening for potential site contamination to determine appropriate future land use. Borehole locations and depths were designed to correctly assess DOE’s future liability for characterizing residual vadose zone contamination. If a residual source area is identified at depth, DOE will need to evaluate the additional cost of future subsurface cleanup (with limited access) due to planned development. This is necessary to leave the location in a “ready-to-build” status. As a result, Phase II sampling requirements may in some cases extend beyond the construction zone required for slab removal to ensure the necessary due diligence. To avoid the potential spread of contamination and/or the creation of an environmental release by impacting the integrity of known contamination areas (e.g., pit within room 102 and the east to west boundary seam), these locations were not sampled for this project. During demolition of the slab, these locations and their underlying soils should be further evaluated. The assessment was conducted in accord with the May 2022 Lawrence Livermore National Laboratory Former Building 175 Assessment Soil Sampling and Analysis Plan / Quality Assurance Plan. Thirty-six, direct-push borings were advanced, along with collecting concrete (where present) and soil samples for laboratory analyses. Thirty-two of the borings were advanced to a depth of 25 feet below ground (bgs), and four borings were advanced to a depth of 55 feet bgs. The concrete slab for former Building 175 ranged from approximately 8 inches to over two feet in thickness. Soils encountered during the investigation consisted primarily of clayey silt, with interbeds of sandy gravel and silty sand to the total depth explored of 55 feet bgs. Field photoionization detector readings – checking for volatile organic compound (VOC) vapors in soils, ranged from zero (0) to a peak of 33 parts per million at 25 feet bgs in boring PC-B175- 028. The cause for the peak reading is unknown, however, no visually discolored or odorous soils were encountered during the assessment and all VOC results were below Lawrence Livermore National Laboratory’s (LLNL’s) Soil Screening and Management Plan (SSMP) soil screening levels (SSL). Groundwater was not encountered during the assessment and is expected to occur at roughly 65 feet bgs in the project area. No metals were detected at concentrations of concern in the concrete core samples collected and analyzed. Low gross alpha and beta activity concentrations were detected in the concrete core samples collected and analyzed. The detected activity concentrations would appear to be from naturally occurring radioactive isotopes, i.e., potassium 40, present in the raw materials used to make concrete and not artificially added. Tritium was not detected above the testing laboratory's Method Detection Limit (MDL) in any of the concrete core samples collected and analyzed. The radioactive isotopes - actinium 228, bismuth 214, lead 212, lead 214, potassium 40, radium 226, radium 228, and thallium 208, were detected at low activity concentrations in the concrete core samples analyzed. Based on the detected activity concentrations, the isotopes would appear to be naturally occurring in the raw materials used to make concrete and not artificially added. Acetone – a common laboratory contaminant, was detected in nine of the soil samples collected and analyzed. Concentrations of two other volatile organic compounds (benzene and tetrachloroethene) were detected in five of the soil samples analyzed. All concentrations were below SSLs. Total petroleum hydrocarbons as diesel range organics were detected at 43.9 milligrams per kilogram (mg/kg) in the 20-foot bgs duplicate sample from boring PC-B175-007. Total petroleum hydrocarbons as motor oil were detected at 7.20 mg/kg in the 15-foot bgs routine sample from boring PC-B175-001, and at 20.7 mg/kg in the 20-foot bgs duplicate sample from boring PC-B175-007. The SSL for diesel-range TPHs is 260 mg/kg. LLNL does not have an SSL for motor oil range TPHs. No indications of a release, e.g., visibly stained, or odorous soil, were present at the boring locations. No samples collected contained polychlorinated biphenyls (PCBs) in concentrations above the laboratory reporting limit. Arsenic was detected above its SSL of 8.51 mg/kg in the 25-foot bgs routine sample collected from boring PC-B175-023. Nickel was detected above its SSL of 86.0 mg/kg in the 10-foot bgs routine sample from boing PC-B175-005, the 20-foot bgs routine sample from boring PC-B175-025, and the 25-foot bgs routine sample from boring PC-B175-034. The detected concentrations, however, were well below ten times (10x) their respective STLCs. Gross alpha and/or gross beta were detected above their respective SSL activity concentrations in three routine soil samples. Retesting (two per sample) of the samples showed that the initial reported activity concentrations were anomalous. Tritium was not detected in any of the routine or duplicate soil samples collected and analyzed during the investigation. The radioactive isotopes - actinium 228, bismuth 212 and 214, lead 212 and 214, potassium 40, radium 224, 226 and 228, thallium 208, thorium 234 and uranium, were detected at low activity concentrations in the soil samples analyzed for radioactive constituents. The detected isotopes and their associated activity concentrations are typical of those naturally occurring in the marine-type sedimentary deposits underlying the Livermore Valley. No radiological controls are necessary for the soil evaluated.
54 ENVIRONMENTAL SCIENCES↗
Energy Capture Avenues for the Wastewater Industry
This information sheet discusses the technology pillar, Energy Capture, as a pathway toward improving wastewater infrastructure sustainability and resiliency. To supplement existing literature on current technologies and policies for improving resiliency at wastewater treatment plants, this document aims to accomplish: • Summarize the existing technologies for the capture and integration of renewable energy and energy storage. • Identify challenges, concerns, and limitations associated with the implementation of technologies. • Serve as a repository for leading approaches to energy capture systems for wastewater utilities. • Present case studies and examples of best practices.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between
Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.
36 MATERIALS SCIENCE↗
Cyber-Informed Engineering Requirements Framework Use Cases
The requirement analysis use case effort leverages the Cyber-Informed Engineering (CIE) requirements framework to examine two real-world scenarios: Battery Energy Storage System (BESS) installation at the Flatirons Campus, NLR; SCADA improvement program. This work evaluates the existing requirements for each use case, applies the CIE requirements framework, and assesses the benefits and enhancements gained compared to the current requirements. The results will advance CIE from concept to practical application by identifying key opportunities for integrating CIE principles into established engineering workflows during the requirements phase.
97 MATHEMATICS AND COMPUTING↗
Sensors with Intelligent Measurement Platform for Monitoring and Control in Distribution Circuits
This paper presents a medium-voltage intelligent measurement platform that provides post-processing of raw measurements received from commercially-available voltage/current sensors to enhance accuracy and bandwidth of the monitoring system. Furthermore, this platform integrates into a Data Distribution Service (DDS) messaging bus for distributed control and interoperability. The authors present the design, specifications, and major components included in the platform, and argue for applications and use cases that most benefit from this new approach. A laboratory testbed has been created to demonstrate the functionality, for which test results are presented for selected test cases.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Resolving ECRH deposition broadening due to edge turbulence in DIII-D
Microwave heat pulse propagation experiments have demonstrated a correlation between millimeter-scale turbulence and deposition profile broadening of electron cyclotron (EC) waves on the DIII-D tokamak. In a set of discharges in DIII-D, a variation in edge density fluctuations on the mm-scale is associated with 40%–150% broader deposition profiles, expressed in terms of normalized minor radius, as compared with equilibrium ray tracing. The 1D power profile is determined from transport analysis of the electron temperature response to EC power modulation using perturbative analysis with a square wave power modulation at 20–70 Hz, producing a series of Fourier harmonics that are fit collectively to resolve transport. Fitting an integrated heat flux expressed in the Fourier basis of the modulation to diffusive, convective, and coupled transport terms in a linear model can resolve the broadened EC deposition width from the power perturbation to resolve a broadening in each case. The best fit degree of beam broadening observed scales approximately linearly with the Doppler backscattering measured fluctuation level in the steep gradient region. Quantifying the effect of edge fluctuation broadening on EC current drive power needs of future devices will require 3D full-wave codes that can be validated on the current generation of machines. These DIII-D experiments provide a quantitative measure of fluctuation effects and a dataset to benchmark full-wave simulations that can model and eventually predict nonlinear effects neglected by 1D equilibrium beam and ray tracing.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗