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At least 649 records · Page 36

Regarding the optimization of O1-mode ECRH and the feasibility of EBW startup on NSTX-U

Recently published scenarios for fully non-inductive startup and operation on the National SphericalTorus eXperiment Upgrade (NSTX-U) (Menard et al 2012 Nucl. Fusion 52 083015) show ElectronCyclotron Resonance Heating (ECRH) as an important component in preparing a target plasma forefficient High Harmonic Fast Wave and Neutral Beam heating. The modeling of the propagation andabsorption of EC waves in the evolving plasma is required to define the most effective window ofoperation, and to optimize the launcher geometry for maximal heating and current drive during thiswindow. Here, we extend a previous optimization of O1-mode ECRH on NSTX-U to account for thefull time-dependent performance of the ECRH using simulations performed with TRANSP. We findthat the evolution of the density profile has a prominent role in the optimization by defining the timewindow of operation, which in certain cases may be a more important metric to compare launcherperformance than the average power absorption. This feature cannot be captured by analysis on staticprofiles, and should be accounted for when optimizing ECRH on any device that operates near thecutoff density. Additionally, the utility of the electron Bernstein wave (EBW) in driving current andgenerating closed flux surfaces in the early startup phase has been demonstrated on a number ofdevices. Using standalone GENRAY simulations, we find that efficient EBW current drive ispossible on NSTX-U if the injection angle is shifted below the midplane and aimed towards the tophalf of the vacuum vessel. However, collisional damping of the EBW is projected to be significant, insome cases accounting for up to 97% of the absorbed EBW power

Electron Bernstein wave↗

The onset distribution of rotating m , n = 2 , 1 tearing modes and its consequences on the stability of high-confinement-mode plasmas in DIII-D

Abstract Analysis of a multi-scenario database of over 13 000 DIII-D H-mode discharges shows that the m , n = 2 , 1 magnetic islands are dominantly pressure gradient driven, stochastically triggered non-linear instabilities at all edge safety factor ( q 95 ) values. The instability onset time closely follows the exponential distribution in intermediate and high q 95 scenarios and is characterized by near constant onset rate ( λ ), in accordance with Poisson-point processes. This implies that the plasmas are operated in marginally stable conditions, characterized by a small threshold for instability growth and variations in the trigger amplitude and/or the stabilizing mechanisms with temporally uniform random distribution in this database. While the majority of the tearing modes occur in the first current-profile relaxation time of the β N flattop, constant λ throughout the β N flattop shows that the tearing onset is insensitive to the evolution of the equilibrium current profile. In low q 95 scenarios, where a large fraction of the plasmas are operated at low torque, λ increases over the course of the β N flattop, showing that these plasmas evolve toward more unstable conditions. The onset rate rapidly increases with β N , while it does not show a clear dependence on the current gradient at the mode rational surface. Overall, these observations support that the majority of the analyzed 2,1 tearing modes are non-linear, neoclassically driven instabilities and classical stability does not play a dominant role in their onset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Depth-Dependent Understanding of Cathode Electrolyte Interphase (CEI) on the Layered Li-Ion Cathodes Operated at Extreme High Temperature

The high-temperature operation of Li-ion batteries is highly dependent on the stability of the cathode electrolyte interphase (CEI) formed during lithiation-delithiation reactions. However, knowledge on the nature of the CEI is limited and its stability under extreme temperatures is not well understood. Therefore, herein, we investigate a proof-of-concept study on stabilizing CEI on model LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC333) at extreme operation condition of 100 oC using thermally stable pyrrolidinium based ionic liquid electrolyte. The electrochemical lithiation-delithiation reactions at 100 oC and the CEI evolution upon different cycling conditions are investigated. Further, the depth-dependent CEI chemistry was investigated using energy tunable synchrotron-based hard X-ray photoelectron spectroscopy (HAXPES). Overall, the results reveal that the high temperature operation accelerated the CEI formation compared to room temperature, and the surface of the interphase layer is rich in boron-based inorganic moieties than the deeper surface. Further, bulk sensitive X-ray absorption spectroscopy (XAS) was used to investigate the transition metal redox contributors during high temperature electrochemical reactions, similar to room temperature, the Ni2+/4+redox couple is the only charge compensating redox couple during high temperature operation. Finally, the physical nature of the conformal CEI on the cathode particles was visualized with high-resolution transmission electron microscopy, which confirms that the significant degradation of cathode particles without conformal CEI is due to the transformation of layer to spinel formation at extreme temperature. In this study, understanding this high temperature interfacial chemistry of NMC cathodes through advanced spectroscopy and microscopy will shed light on transforming ambient temperature Li-ion chemistry to high temperature applications.

25 ENERGY STORAGE↗

Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks

Artificial intelligence techniques have been increasingly adopted by the plasma and fusion science to address problems like plasma reconstruction, surrogate modeling, and tokamak/stellarator optimization. A key focus in sustained fusion research is the prediction and mitigation of edge-localized-modes (ELMs), instabilities that occur in short, periodic bursts and can cause erosion to the tokamak vessel wall. Recent research has demonstrated the power of neural networks in approximating continuous functions. In this work, we build spatiotemporal forecasting models that can predict the onset of ELMs and their evolution at early stages. We leverage recent advances in generative modeling, sequence-to-sequence modeling, and Fourier neural operators to propose architectures and training strategies that can learn to forecast short to long term dynamics of the noisy signals due to ELMs. We benchmark the developed model against a state-of-the-art foundation model using the beam emission spectroscopy (BES) data that captures the plasma fluctuations due to ELMs over a 8 x 8 spatial grid. Our models demonstrate high accuracy, outperforming the baselines, in predicting the evolution of BES signals during ELM events. Furthermore, the developed models exhibit high accuracy in predicting the rapid rise and relaxation of the signals due to ELMs within 30–80 µs.

edge localized modes↗

Characterization of SiO 2 Thermally Grown Oxide Stress Evolution of EBCs with Al-Containing Dopants

SiC/SiC ceramic matrix composites (CMCs) are desired for use in combustion environments to achieve higher turbine operating temperatures. However, CMCs require environmental barrier coatings (EBCs) for protection from the gas environment. EBC systems are known to primarily fail through coating delamination via growth of a thermally grown oxide (TGO) at the EBC—silicon bond coating interface when exposed to steam, which accelerates the TGO growth rate. The TGO undergoes a phase transformation during thermal cycling, which results in stresses that may encourage EBC spallation. Yb-silicate EBCs with mullite and yttrium aluminum garnet (YAG) dopant additions were deposited on SiC substrates with a Si intermediate bond coating and exposed to thermal cycling in steam at 1350 °C. The impact of Al dopant additions on the TGO growth rate and the SiO 2 phase transformation was assessed. Photo-stimulated luminescence spectroscopy (PSLS) was used to characterize the Al-containing phases and to measure stress evolution in the EBC following exposure using the stress-induced peak shift of the R-lines of mullite. Raman microscopy was used to map the stresses in the Si bond coating following exposure. It was found that the TGO phase transformation upon cooling increased compressive stress in the Si bond coating within 15 µm of the TGO.

Building Materials↗

A percolating path to green iron

About 1.9 gigatons of steel is produced every year, emitting 8% (3.6 gigatons) of global CO 2 in the process. More than 50% of the CO 2 emissions come from a single step of steel production, known as ironmaking. Hydrogen- based direct reduction (HyDR) of iron oxide to iron has emerged as an emission-free ironmaking alternative. However, multiple physical and chemical phenomena ranging from nanometers to meters inside HyDR reactors alter the microstructure and pore networks in iron oxide pellets, in ways that resist gaseous transport of H 2 /H 2 O, slow reaction rates, and disrupt continuous reactor operation. Using synchrotron nano X-ray computed tomography and percolation theory, we quantify the evolution of pores in iron oxide pellets and demonstrate how nanoscale pore connectivity influences micro- and macroscale flow properties such as permeability, diffusivity, and tortuosity. Our modeling framework connects disparate scales and offers opportunities to accelerate HyDR.

hydrogen↗

Dynamical properties of a driven dissipative dimerized $S$ =1/2 chain

We consider the dynamical properties of a gapped quantum spin system coupled to the electric field of a laser, which drives the resonant excitation of specific phonon modes that modulate the magnetic interactions. Here, we deduce the quantum master equations governing the time-evolution of both the lattice and spin sectors, by developing a Lindblad formalism with bath operators providing an explicit description of their respective phonon-mediated damping terms. We investigate the nonequilibrium steady states (NESS) of the spin system established by a continuous driving, delineating parameter regimes in driving frequency, damping, and spin-phonon coupling for the establishment of physically meaningful NESS and their related nontrivial properties. Focusing on the regime of generic weak spin-phonon coupling, we characterize the NESS by their frequency and wave-vector content, explore their transient and relaxation behavior, and discuss the energy flow, the system temperature, and the critical role of the type of bath adopted. Our study lays a foundation for the quantitative modeling of experiments currently being designed to control coherent many-body spin states in quantum magnetic materials.

1-dimensional spin chains↗

Postselection-Free Entanglement Dynamics via Spacetime Duality

The dynamics of entanglement in 'hybrid' non-unitary circuits (for example, involving both unitary gates and quantum measurements) has recently become an object of intense study. A major hurdle toward experimentally realizing this physics is the need to apply postselection on random measurement outcomes in order to repeatedly prepare a given output state, resulting in an exponential overhead. We propose a method to sidestep this issue in a wide class of non-unitary circuits by taking advantage of spacetime duality. This method maps the purification dynamics of a mixed state under non-unitary evolution onto a particular correlation function in an associated unitary circuit. This translates to an operational protocol which could be straightforwardly implemented on a digital quantum simulator. We discuss the signatures of different entanglement phases, and demonstrate examples via numerical simulations. With minor modifications, the proposed protocol allows measurement of the purity of arbitrary subsystems, which could shed light on the properties of the quantum error correcting code formed by the mixed phase in this class of hybrid dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗

Exploratory Modeling and Simulation of the Evolutionary Dynamics of Single-Stranded RNA Virus Populations

Infection, replication and mutation govern the population dynamics of viruses and are the key mechanisms driving their evolution. In particular, RNA viruses (such as the causative agents of Ebola, Dengue, Zika, West Nile, and SARS) have the highest mutation rates which enable them to form highly diverse populations within a single host, evade immune responses and develop resistances to drugs. Understanding the complexity of virus evolution is crucial for developing reliable countermeasures. We present an exploratory simulation model to study the evolution of heterogeneous virus populations in heterogeneous cell environments. This is a unique model that operates at three scales and captures the core mechanisms of the evolutionary process. To the best of our knowledge, this is the first HPC-based simulation of its kind.

60 APPLIED LIFE SCIENCES↗

Effects of repetitive pulsing on multi-kHz planar laser-induced incandescence imaging in laminar and turbulent flames

Planar laser-induced incandescence (LII) imaging is reported at repetition rates up to 100 kHz using a burst-mode laser system to enable studies of soot formation dynamics in highly turbulent flames. Furthermore, to quantify the accuracy and uncertainty of relative soot volume fraction measurements, the temporal evolution of the LII field in laminar and turbulent flames is examined at various laser operating conditions. Under high-speed repetitive probing, it is found that LII signals are sensitive to changes in soot physical characteristics when operating at high laser fluences within the soot vaporization regime. For these laser conditions, strong planar LII signals are observed at measurement rates up to 100 kHz but are primarily useful for qualitative tracking of soot structure dynamics. However, LII signals collected at lower fluences allow sequential planar measurements of the relative soot volume fraction with a sufficient signal-to-noise ratio at repetition rates of 10–50 kHz. Finally, guidelines for identifying and avoiding the onset of repetitive probe effects in the LII signals are discussed, along with other potential sources of measurement error and uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Evaluating the Interactions Between Variable Renewable Energy and Diurnal Storage

Cost declines and growing deployment of photovoltaic (PV), wind, and storage have led to increasing interest in the potential interactions of these three technologies as their role in the power system grows. In this work we enhance a national-scale capacity expansion model to evaluate how PV, wind, and storage interact in the evolution of the power system. Importantly, the modeling framework captures interactions in both investments and operations. Through this work we identify significant synergies between PV and storage. Scenarios with more PV always have more storage, and scenarios with more storage always have more PV. This synergy is due to the diurnal alignment of PV generation with 4-8 hour storage, and to the ability of PV to narrow system peaks to allow shorter-duration storage to serve as a peaking resource. Interactions between wind and storage are less pronounced, though we do observe that longer-duration storage resources appear to provide greater value for wind.

14 SOLAR ENERGY↗

Numerical study of proppant transport and settling processes in fractures

Reservoir stimulation by creating hydraulically conductive fractures is the key step for enabling enhanced geothermal systems (EGS). The effectiveness of stimulation is significantly influenced by the deposition of proppant inside induced fractures. The transportation and settling of proppant in a propagating fracture is controlled by a multitude of operational and physical parameters, including the fracturing fluid rheology, injection rate, proppant concentration, fracture length/aperture evolution, proppant size/density/shape, etc. A numerical tool that robustly and efficiently accounts for all important attributes can facilitate the design and optimization of reservoir stimulation. This study presents the novel computational tool ELK (ELectrical fracKing) developed for the numerical simulation of proppant-fluid mixture circulation in a fractured geothermal reservoir. We enriched the MOOSE-based PorousFlow module with a suite of equations to consider the fluid-proppant mixture with particle-particle/fluid interactions, which include gravitational settling, particle convection, particle hampering, and strong density and viscosity contrasts. The computational tool is validated by comparing the predicted proppant bed evolution against two different laboratory scale experiments of proppant transport in a fixed aperture channel. Further parameter studies were performed, and the modeling results show that the proppant deposition is determined by the mixing characteristics and settling of the particles from the slurry. Concentration-dependent density and viscosity lead to an inhomogeneous distribution of the proppant, particle collision, and enhanced settling at the bottom of the fractures. Preliminary coupling with dynamic fracture propagation shows promising results and will be further developed to simulate hydraulic stimulation at high fidelity.

15 GEOTHERMAL ENERGY↗

Management of Irradiated Control Rods: Cost of an Industrial Approach - 18009

This paper concerns the sorting of irradiated control rods that can be done to limit the volume of Intermediate Level Long-lived Waste. More specifically it assesses the cost of an industrial approach. For this we used numerical simulations, penalizing hypotheses and measurements. The cost is given in term of the increased volume of the control rods placed into deep geological storage. To calculate the radioactive inventories, we have developed a calculation scheme which includes a mapping of the neutron flux, a calculation of the isotopic compositions evolution, and a validation of the results. For the state of the art simulations, all operational fuel loading patterns were considered and coupled with all effective power distributions. The analysis of the calculated results shows that the simulations produce a slight overestimation of the measurements. For envelope simulations, penalizing fuel loading patterns and nominal power distribution conditions were used. Most of the radionuclides needed to declare the radiological inventories are difficult to measure. Therefore, for each radionuclide a scaling factor referred to a key radionuclide, can be calculated. Then during the dismantling operations these calculated scaling factors are multiplied by the measured key radionuclide to determine the radiological inventory. The industrial approach consists of combining envelope simulations with scaling factors. Applied to PWR control rods, the industrial approach overestimates the radiological inventories by a factor of 2. As such, the calculated position of the Intermediate Level Long Lived part limit moves +6 cm. Compared to the 1 meter of the control rod pins classified as Intermediate Level Long Lived, the industrial approach would increase by 6 % the volume of the control rods placed into the deep geological storage. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Data-driven Resilience Characterization of Control Dynamical Systems

In this paper, we define and quantify resiliency of a power network and propose data-driven algorithms for computing the same for the power grid. To do this, we use the Koopman operator framework to lift the controlled dynamical system to an abstract (possibly higher) dimensional space, where the evolution is linear. The linear system representation allows us to relate small time local controllability and observability of a general nonlinear control system to the controllability and observability of the lifted linear system. Finally, we define the resiliency of the underlying power grid in terms of the controllability and observability gramians of the lifted linear system. We illustrate the proposed approach to compute the resiliency metrics on time-series data obtained from a microgrid.

koopman operator, resilience, control↗

Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking

Abstract. To probe the potential value of a radar-driven field campaign to constrain simulation of isolated convection subject to a strong aerosol perturbation, convective cells observed by the operational KHGX weather radar in the vicinity of Houston, Texas, are examined individually and statistically. Cells observed in a single case study of onshore flow conditions during July 2013 are first examined and compared with cells in a regional model simulation. Observed and simulated cells are objectively identified and tracked from observed or calculated positive specific differential phase ( K DP ) above the melting level, which is related to the presence of supercooled liquid water. Several observed and simulated cells are subjectively selected for further examination. Below the melting level, we compare sequential cross sections of retrieved and simulated raindrop size distribution parameters. Above the melting level, we examine time series of K DP and radar differential reflectivity ( Z DR ) statistics from observations and calculated from simulated supercooled rain properties, alongside simulated vertical wind and supercooled rain mixing ratio statistics. Results indicate that the operational weather radar measurements offer multiple constraints on the properties of simulated convective cells, with substantial value added from derived K DP and retrieved rain properties. The value of collocated three-dimensional lightning mapping array measurements, which are relatively rare in the continental US, supports the choice of Houston as a suitable location for future field studies to improve the simulation and understanding of convective updraft physics. However, rapid evolution of cells between routine volume scans motivates consideration of adaptive scan strategies or radar imaging technologies to amend operational weather radar capabilities. A 3-year climatology of isolated cell tracks, prepared using a more efficient algorithm, yields additional relevant information. Isolated cells are found within the KHGX domain on roughly 40 % of days year-round, with greatest concentration in the northwest quadrant, but roughly 5-fold more cells occur during June through September. During this enhanced occurrence period, the cells initiate following a strong diurnal cycle that peaks in the early afternoon, typically follow a south-to-north flow, and dissipate within 1 h, consistent with the case study examples. Statistics indicate that ~  150 isolated cells initiate and dissipate within 70 km of the KHGX radar during the enhanced occurrence period annually, and roughly 10 times as many within 200 km, suitable for multi-instrument Lagrangian observation strategies. In addition to ancillary meteorological and aerosol measurements, robust vertical wind speed retrievals would add substantial value to a radar-driven field campaign.

54 ENVIRONMENTAL SCIENCES↗

Meso-Scale Fuel Performance Modeling using the MARMOT Code

The in-pile behavior of light-water-reactor (LWR) fuels is governed by the physics from various time and spatial scales. Accordingly, multi-scale modeling and simulation are needed to accurately predict the fuel performance at the engineering scale. To improve the traditional fuel performance modeling, the Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program targets at developing multi-scale, multiphysics modeling tools for in-pile fuel behavior prediction under various operating or accident scenarios. In traditional fuel performance modeling, the history of fuel is represented by a single parameter, fuel burn-up. Burn-up measures the energy that has been extracted from fuels. However, it is not an adequate measure of fuel history as it ignores the flux effect, which is critical for the irradiation-induced microstructural evolution in fuels. Moreover, the traditional fuel performance models are usually fitted to experimental data. Their capabilities of being extrapolated to new operating conditions and being extended to new types of fuels are strongly limited by the empirical or semi-empirical nature. To improve the burn-up based models, a microstructure based fuel approach, the MOOSE-BISON-MARMOT (MBM) tool-kit, is being developed under the fuel product line (FPL) of the NEAMS program for predictive fuel performance modeling. In the MBM approach, the evolution of fuel microstructure and changes in fuel properties are described by the MARMOT code. The transient fuel properties obtained from MARMOT are then fed into the BISON code for engineering scale fuel performance modeling. Both BISON and MARMOT are developed within the MOOSE framework, which was developed at Idaho National Laboratory. In this summary, the development and the capabilities of the MARMOT code will be introduced. In the past few years, several materials models have been developed including: (1) a grain growth model that describes the grain size evolution as a function of time, temperature and fuel porosity, considering the effects of impurity and porosity dragging and anisotropic grain boundary energy, (2) a fission gas release model that describes the fission gas diffusion in grain interior, fission gas bubble formation in grain interior and at grain boundaries, and fission gas release to the fuel-cladding gap, (3) a fracture model that describes the fracture initiation and propagation in grain interior and along grain boundaries, and (4) a thermal conductivity model that describes the change in thermal conductivity due to grain growth, gas bubble formation, fracture and radiation damage. Several other models are also being developed in MARMOT for hydride formation in Zr-based cladding, radiation damage and grain size subdivision. In most cases, these materials models are coupled with each other. For instance, to accurate predict the transient thermal conductivity, information on grain size, fission gas density, porosity and fracture is required. Further development is on going to improve all these models. These models are developed primarily for UO{sub 2} fuels used in LWRs. They can be extended to other fuels with possibly new development. All models in MARMOT will be rigorously verified and validated before being applied for fuel performance modeling. A version of MARMOT was recently released in September 2015 with an assessment report. (authors)

36 MATERIALS SCIENCE↗

Towards A Theory of Autonomous Reconstitution of Compromised Cyber-Systems

The ability to maintain mission-critical operations in cyber-systems in the face of disruptions is critical. Faults in cyber systems can come from accidental sources (e.g., natural failure of a component) or deliberate sources (e.g., an intelligent adversary). Natural and intentional manipulation of data, computing, or coordination are the most impactful ways that an attacker can prevent an infrastructure from realizing its mission goals. Under these conditions, the ability to reconstitute critical infrastructure becomes important. Specifically, the question is: Given an intelligent adversary, how can cyber systems respond to keep critical infrastructure operational? In cyber systems, the distributed nature of the system poses serious difficulties in maintaining operations, in part due to the fact that a centralized command and control apparatus is unlikely to provide a robust framework for resilience. Resilience in cyber-systems, in general, has several components, and requires the ability to anticipate and withstand attacks or faults, as well as recover from faults and evolve the system to improve future resilience. The recovery effort (and any subsequent evolution) may require significant reconfiguration of the system (at all levels – hardware, software, services, permissions, etc.) if the system is to be made resilient to further attack or faults. This is especially important in the case of ongoing attacks, where reconfiguration decisions must be taken with care to avoid further compromising the system while maintaining continuity of operations. Collectively, we will label this recovery and evolution process as “reconstitution”. Currently, reconstitution is performed manually, generally after-the-fact, and usually consists of either standing up redundant systems, check-points (rolling back the configuration to a “clean” state), or re-creating the system using “gold-standard” copies. For enterprise systems, such reconstitution may be performed either directly on hardware, or using virtual machines. A significant challenge within this context is the ability to verify that the reconstitution is performed in a manner that renders the cyber-system resilient to ongoing and future attacks or faults. Fundamentally, the need is to determine optimal configuration of the cyber system when a fault is determined to be present. While existing theories for fault tolerance (for example, Byzantine fault tolerance) can guarantee resilience under certain conditions, in practice, these theories can break down in the face of an intelligent adversary. Further, it is difficult, in a dynamically evolving environment, to determine whether the necessary conditions for resilience have been met, resulting in difficulties in achieving resilient operation. In addition, existing theories do not sufficiently take into account the cost for attack and defense (the adversary is generally assumed to have infinite resources and time), hierarchy of importance (all network resources are assumed to be equally important), and the dynamic nature of some attacks (i.e., as the attack evolves, can resilience be maintained?). Alternative approaches to resilience based on a centralized command and control structure suffer from a single-point-failure. This paper presents preliminary research towards concepts for effective autonomous reconstitution of compromised cyber systems. We describe a mathematical framework as a first step towards a theoretical basis for autonomous reconstitution in dynamic cyber-system environments. We then propose formulating autonomous reconstitution as an optimization problem and describe some of the challenges associated with this formulation. This is followed by a brief discussion on potential solutions to these challenges.

asymmetric resilience↗

Physics-based control-oriented modeling of the current density profile evolution in NSTX-Upgrade

Active control of the toroidal current density profile is among those plasma control milestones that the National Spherical Torus eXperiment-Upgrade (NSTX-U) program must achieve to realize its next-step operational goals. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards control design is the development of a physics-based, control-oriented model for the current profile evolution in response to non-inductive current drives and heating systems. The evolution of the toroidal current density profile is closely related to the evolution of the poloidal magnetic flux profile, whose dynamics is modeled by a nonlinear partial differential equation (PDE) referred to as the magnetic-flux diffusion equation (MDE). The proposed control-oriented model predicts the spatial-temporal evolution of the current density profile by combining the nonlinear MDE with physics-based correlations obtained at NSTX-U for the electron density, electron temperature, and non-inductive current drives (neutral beams). The resulting first-principles-driven, control-oriented model is tailored for NSTX-U based on predictions by the time-dependent transport code TRANSP. Furthermore, main objectives and possible challenges associated with the use of the developed model for the design of both feedforward and feedback controllers are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗