Dynamic measurement of impurity ion transport in runaway electron plateaus in DIII-D
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Historically, there has been little faith in particle tracking velocimetry (PTV) as a tool to make quantitative measurements of thermal counterflow in He II, since tracer particle motion is complicated by influences from the normal fluid, superfluid, and quantized vortex lines, or a combination thereof. Recently, we introduced a scheme for differentiating particles trapped on vortices (G1) from particles entrained by the normal fluid (G2). In this paper, we apply this scheme to demonstrate the utility of PTV for quantitative measurements of vortex dynamics in He II counterflow.We estimate Ι, the mean vortex line spacing, using G2 velocity data, and c 2 , a parameter related to the mean curvature radius of vortices and energy dissipation in quantum turbulence, using G1 velocity data. We find that both estimations show good agreement with existing measurements that were obtained using traditional experimental methods. This is of particular consequence since these parameters likely vary in space, and PTV offers the advantage of spatial resolution. Furthermore, we also show a direct link between power-law tails in transverse particle velocity probability density functions (PDFs) and reconnection of vortex lines on which G1 particles are trapped.
Integrable models are characterized by the existence of stable excitations that can propagate indefinitely without decaying. This includes multimagnon bound states in the celebrated 𝑋𝑋𝑍 spin-chain model and its integrable Floquet counterpart. A recent Google Quantum AI experiment [A. Morvan et al., Nature 612, 240 (2022)] realizing the Floquet model has demonstrated the persistence of such collective excitations even when the integrability is broken: this observation is at odds with the expectation of ergodic dynamics in generic nonintegrable systems. Here, we study the spectrum of the model realized in the experiment using exact diagonalization and physical arguments. We find that isolated bands corresponding to the descendants of the exact bound states of the integrable model are clearly observable in the spectrum for a large range of system sizes. However, our numerical analysis of the localization properties of the eigenstates suggests that the bound states become unstable in the thermodynamic limit. A perturbative estimate of the decay rate agrees with the prediction of an eventual instability for large system sizes.
The Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) will provide orbit determination and prediction support for the Magnetospheric Multiscale (MMS) mission during the mission's commissioning period. The spacecraft will launch into a highly elliptical Earth orbit in 2015. Starting approximately four days after launch, a series of five large perigee-raising maneuvers will be executed near apogee on a nearly every-other-orbit cadence. This perigee-raise operations concept requires a high-accuracy estimate of the orbital state within one orbit following the maneuver for performance evaluation and a high-accuracy orbit prediction to correctly plan and execute the next maneuver in the sequence. During early mission design, a linear covariance analysis method was used to study orbit determination and prediction accuracy for this perigee-raising campaign. This paper provides a higher fidelity Monte Carlo analysis using the operational COTS extended Kalman filter implementation that was performed to validate the linear covariance analysis estimates and to better characterize orbit determination performance for actively maneuvering spacecraft in a highly elliptical orbit. The study finds that the COTS extended Kalman filter tool converges on accurate definitive orbit solutions quickly, but prediction accuracy through orbits with very low altitude perigees is degraded by the unpredictability of atmospheric density variation.
For solar electricity generating technologies to be cost competitive at a large scale with conventionally generated electricity, cost reductions are needed for both concentrating solar power (CSP) and solar photovoltaic (PV) systems. In 2011, the U.S. Department of Energy (DOE) established solar cost targets that corresponded to reducing CSP and PV prices by approximately 75% in order to achieve a levelized cost of electricity (LCOE) of $0.06 per kilowatt-hour (kWh) for both utility-scale PV and high-capacity factor CSP-TES systems in 2020. Utility-scale PV achieved its 2020 cost target in 2017, and recent estimates for the LCOE of CSP-TES with a molten-salt power tower system are approximately $0.10/kWh for projects that are expected to come online in 2020, which represents a substantial reduction since 2010 - when the LCOE for CSP-TES was around $0.21/kWh. To continue the momentum for cost reductions in solar technologies, DOE recently established cost targets for 2030 that would make solar one of the lowest-cost sources of new electricity in the United States. For CSP-based systems, the new targets correspond to an LCOE in 2030 of $0.05/kWh for a dispatchable, high-capacity factor CSP-TES plant configuration. This aggressive target would have been unimaginable a decade ago. However, building on the previously described reduction in CSP-TES costs over the past decade, recent announcements suggest the next phase of projects will continue this downward trend through lower installation costs, attractive financing, longer-duration PPAs, and the ability to capitalize on the value that the flexibility of storage brings CSP. This presentation summarizes a recent report that evaluates the potential impacts of simultaneously achieving the 2030 cost targets for PV and CSP-TES, and it includes a detailed evaluation of the role that CSP-TES could play in realizing those impacts. The scenarios in this analysis are designed to isolate and assess the potential impacts of achieving DOE's 2030 cost targets for CSP-TES and PV, and they do not reflect the potential benefits or system impacts associated with success in other DOE research programs. The low-cost solar scenarios include DOE's 2030 solar cost targets--which are represented via a roughly 50% reduction in LCOE by 2030 (from current levels) with additional cost reductions thereafter representing technology learning and/or improvements that could result from innovation - coupled with a variety of market, technology, and demand assumptions. With these assumptions, the evolution of the contiguous U.S. electricity system is evaluated with NREL's Renewable Energy Deployment System (ReEDS) model, which was specifically designed to represent the temporal and locational value of renewable generation technologies in the U.S. power system. ReEDS relies on system-wide least-cost optimization to estimate the type and location of future generation and transmission capacity. In addition, it accounts for the locational and temporal variations in variable renewable technologies, including the need for new transmission, curtailment, dynamic capacity value, and the need to hold operating reserves to account for the uncertainty and variability of these technologies. This presentation summarizes the key findings that arise from a detailed evaluation of the impacts of achieving DOE's 2030 cost targets for CSP-TES and PV systems, noting again the inherent challenges associated with modeling future scenarios of the large, complex system electricity system in the contiguous United States within the context of various market features.
Thick rubber components are employed by the Army to carry large loads. In tanks, rubber covers road wheels and track systems to protect roadways. It is difficult for design engineers to simulate the details of the hysteretic heating for large strain viscoelastic deformations. In this study, an approximation to the viscoelastic energy dissipated per unit time is investigated for use in estimating mechanically induced viscoelastic heating. Coupled thermo-mechanical simulations of large cyclic deformations of rubber cylinders are presented. The cylinders are first compressed axially and then cyclically loaded about the compressed state. Details of the algorithm and some computational issues are discussed. The coupled analyses are conducted for tall and short rubber cylinders both with and without imbedded metal disks.
The interaction between edge dislocations and Guinier-Preston zones in an Al-Cu alloy was analyzed by means of atomistic simulations. The different thermodynamic functions that determine the features of these obstacles for the dislocation glide were computed using molecular statics, molecular dynamics and the nudged elastic band method. It was found that Guinier-Preston zones are sheared by dislocations and the rate at which dislocations overcome the precipitate is controlled by the activation energy, Δ U , in agreement with the postulates of the harmonic transition state theory. Moreover, the entropic contribution to the Helmholtz activation free energy was in the range 1.3–1.8 k b , which can be associated with the typical vibrational entropy of solids. Finally, an estimation of the initial shear flow stress as a function of temperature was carried out from the thermodynamic data provided by the atomistic simulations. Furthermore, comparison with experimental results showed that the effect of the random precipitate distribution and of the dislocation character and dislocation/precipitation orientation has to be taken into account in the simulations to better reproduce experiments.
We have supplemented our code, which computes the evolution of the physical state of a representative piece of the universe to include, not only the dynamics of dark matter (with a standard PM code), and the hydrodynamics of the gaseous component (including detailed collisional and radiative processes), but also galaxy formation on a heuristic but plausible basis. If, within a cell the gas is Jeans' unstable, collapsing, and cooling rapidly, it is transformed to galaxy subunits, which are then followed with a collisionless code. After grouping them into galaxies, we estimate the relative distributions of galaxies and dark matter and the relative velocities of galaxies and dark matter. In a large scale CDM run of 80/h Mpc size with 8 x 10 exp 6 cells and dark matter particles, we find that physical bias b is on the 8/h Mpc scale is about 1.6 and increases towards smaller scales, and that velocity bias is about 0.8 on the same scale. The comparable HDM simulation is highly biased with b = 2.7 on the 8/h Mpc scale. Implications of these results are discussed in the light of the COBE observations which provide an accurate normalization for the initial power spectrum. CDM can be ruled out on the basis of too large a predicted small scale velocity dispersion at greater than 95 percent confidence level.
Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.
In this paper, we propose a method for generating dynamically feasible trajectories for an acoustically aware vehicle with propeller phase control. The trajectory generation procedure allows both propeller phase control and navigation objectives to be considered simultaneously. The presented method is demonstrated where the mission objectives are given as a desired position and phase trajectory. From these trajectories, the full desired state of the vehicle is calculated. Furthermore, the control inputs that realize the desired mission objectives are computed. The acoustic performance for the given trajectory is estimated in terms of sound pressure level as a function of tracking performance. The method is demonstrated in simulation, where the vehicle must navigate through an urban environment with both spatial and acoustic constraints. In the presented scenario, the vehicle must follow a given flight path, and can only reduce sound pressure level by changing the propeller phase targets.
In this paper, we propose a method for generating dynamically feasible trajectories for an acoustically aware vehicle with propeller phase control. The trajectory generation procedure allows both propeller phase control and navigation objectives to be considered simultaneously. The presented method is demonstrated where the mission objectives are given as a desired position and phase trajectory. From these trajectories, the full desired state of the vehicle is calculated. Furthermore, the control inputs that realize the desired mission objectives are computed. The acoustic performance for the given trajectory is estimated in terms of sound pressure level as a function of tracking performance. The method is demonstrated in simulation, where the vehicle must navigate through an urban environment with both spatial and acoustic constraints. In the presented scenario, the vehicle must follow a given flight path, and can only reduce sound pressure level by changing the propeller phase targets.
We describe a helium evaporation refrigerator used to cool dynamically polarized proton and deuteron targets for electron-scattering experiments using the CEBAF Large Acceptance Spectrometer CLAS12 at Jefferson Lab. The geometry of the CLAS12 detector systems places severe design and construction constraints on the refrigerator and its ancillary equipment, resulting in a horizontal cryostat with a length of 4 m. The 16 cm 3 target samples, consisting of frozen ammonia (NH 3 or ND 3 ), are loaded at the upstream end of the cryostat and moved to the beam-interaction region using a novel transport mechanism. At this location they are cooled with superfluid helium and polarized via dynamic nuclear polarization at 1 K and 5 T. In this manner samples can be replaced and cooled to 1 K in about 30 minutes without disturbing any elements of the electron beam line or particle detection system. In conclusion, we estimate that this method saved 18 days of valuable beam time over the course of a recent, 88-day long experiment.
Aquatic ecosystem metabolism encapsulates the daily fixation (gross primary production, GPP d ) and mineralization (ecosystem respiration, ER d ) of organic carbon. In fluvial systems, these are commonly estimated by inverse solutions to field observations using a model that describes oxygen concentrations varying in the water column in response to metabolic fluxes and air‐water gas exchange controlled by a rate coefficient (K 600 ). The most common conceptual model is the single‐station metabolism (SSM) model. The simplicity and flexibility of this conceptualization make it attractive; however, it implicitly assumes that all the processes that consume oxygen in fluvial systems can be lumped into a bulk estimate of respiration with poorly understood consequences for estimates of GPP d , ER d , and K 600 . Here, we focus on the implications of using SSM conceptualization when estimating metabolic fluxes from oxygen dynamics in channels where hyporheic exchange occurs. We use a new multiscale numerical model for reactive transport in streams that represents hyporheic exchange and streambed heterotrophic respiration. Nondimensionalization of this model reveals dimensionless groups that collectively control oxygen dynamics. Numerical experiments offer a mechanistic understanding of the impacts of hyporheic exchange on diel oxygen dynamics revealing that potential biases arise from neglecting mass transfer limitations. Specifically, we found that hyporheic exchange significantly affects diel oxygen dynamics, even for nonreactive streambed sediments. Moreover, while the SSM performs well in many situations, we find conditions where significant bias is produced by hyporheic exchange, even when oxygen data are well‐fitted. These situations pose a major challenge in the interpretation of metabolism assessment estimates.
The goal of this internship is to develop a fluid dynamics characterization of the high-pressure flow used for cleaning the internal surface of the SRF cavities. Initially, the High Pressure Rinsing (HPR) process is introduced, focusing on the problems of the SRF cavities and how this method can be used to increase their acceleration efficiency and performance. The characterization of the problem is then presented, including an introduction to the multiphase flow modeling and the analysis of the Fermilab facility for the HPR. To accomplish the goal, a preliminary estimation of the variables was performed using MATLAB. Then, multiple 2D simulations were developed with Ansys Fluent, and the influence of the main parameter was investigated. The simulation results are then presented and analysed, with a focus on the possible future developments of the HPR process fluid dynamic characterization.
The goal of 'Estimate' is to take advantage of attitude information to produce better pose while staying flexible and robust. Currently there are several instruments that are used for attitude: gyros, inclinometers, and compasses. However, precise and useful attitude information cannot come from one instrument. Integration of rotational rates, from gyro data for example, would result in drift. Therefore, although gyros are accurate in the short-term, accuracy in the long term is unlikely. Using absolute instruments such as compasses and inclinometers can result in an accurate measurement of attitude in the long term. However, in the short term, the physical nature of compasses and inclinometers, and the dynamic nature of a mobile platform result in highly volatile and therefore useless data. The solution then is to use both absolute and relative data. Kalman Filtering is known to be able to combine gyro and compass/inclinometer data to produce stable and accurate attitude information. Since the model of motion is linear and the data comes in as discrete samples, a Discrete Kalman Filter was selected as the core of the new estimator. Therefore, 'Estimate' can be divided into two parts: the Discrete Kalman Filter and the code framework.
Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to evaluate actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.
The control of linear continuous dynamical systems is investigated as a problem of limited state feedback control. The equations which describe the structure of an observer are developed constrained to time-invarient systems. The optimal control problem is formulated, accounting for the uncertainty in the design parameters. Expressions for bounds on closed loop stability are also developed. The results indicate that very little uncertainty may be tolerated before divergence occurs in the recursive computation algorithms, and the derived stability bound yields extremely conservative estimates of regions of allowable parameter variations.
A new methodology is described for the design of nonlinear dynamic controllers for nonlinear multivariable systems providing guarantees of closed-loop stability, performance, and robustness. The methodology is an extension of the Linear-Quadratic-Gaussian with Loop-Transfer-Recovery (LQG/LTR) methodology for linear systems, thus hinging upon the idea of constructing an approximate inverse operator for the plant. A major feature of the methodology is a unification of both the state-space and input-output formulations. In addition, new results on stability theory, nonlinear state estimation, and optimal nonlinear regulator theory are presented, including the guaranteed global properties of the extended Kalman filter and optimal nonlinear regulators.