High-fidelity simulation of an ultrasonic standing-wave thermoacoustic engine with bulk viscosity effects

We have carried out boundary-layer-resolved, unstructured fully-compressible Navier--Stokes simulations of an ultrasonic standing-wave thermoacoustic engine (TAE) model. The model is constructed as a quarter-wavelength engine, approximately 4 mm by 4 mm in size and operating at 25 kHz, and comprises a thermoacoustic stack and a coin-shaped cavity, a design inspired by Flitcroft and Symko (2013). Thermal and viscous boundary layers (order of 10 $\mathrm{\mu}$m) are resolved. Vibrational and rotational molecular relaxation are modeled with an effective bulk viscosity coefficient modifying the viscous stress tensor. The effective bulk viscosity coefficient is estimated from the difference between theoretical and semi-empirical attenuation curves. Contributions to the effective bulk viscosity coefficient can be identified as from vibrational and rotational molecular relaxation. The inclusion of the coefficient captures acoustic absorption from infrasonic ($\sim$10 Hz) to ultrasonic ($\sim$100 kHz) frequencies. The value of bulk viscosity depends on pressure, temperature, and frequency, as well as the relative humidity of the working fluid. Simulations of the TAE are carried out to the limit cycle, with growth rates and limit-cycle amplitudes varying non-monotonically with the magnitude of bulk viscosity, reaching a maximum for a relative humidity level of 5%. A corresponding linear model with minor losses was developed; the linear model overpredicts transient growth rate but gives an accurate estimate of limit cycle behavior. An improved understanding of thermoacoustic energy conversion in the ultrasonic regime based on a high-fidelity computational framework will help to further improve the power density advantages of small-scale thermoacoustic engines.Comment: 55th AIAA Aerospace Sciences Meeting, AIAA SciTech, 201

Robust multivariable predictive control: how can it be applied to industrial test stands ?

To cope with recent technological evolutions of air conditioning systems for aircraft, the French Aeronautical Test Center built a new test stand for certification at ground level. The constraints specified by the industrial users of the process seemed antagonistic for many reasons. First, the controller had to be implemented on an industrial automaton, not adaptable to modern algorithms. Then the specified dynamic performances were very demanding, especially taking into account the wide operating ranges of the process. Finally, the proposed controller had to be easy for nonspecialist users to handle. Thus, the control design and implementation steps had to be conducted considering both theoretical and technical aspects. This finally led to the development of a new multivariable predictive controller, called alpha-MPC, whose main characteristic is the introduction of an extra tuning parameter alpha that has enhanced the overall control robustness. In particular, the H1-norm of the sensitivity functions can be significantly reduced by tuning this single new parameter. It turns out to be a simple but efficient way to improve the robustness of the initial algorithm. The other classical tuning parameters are still physically meaningful, as is usual with predictive techniques. The initial results are very promising and this controller has already been adopted by the industrial users as the basis of the control part for future developments of the test stand

A technology development program for large space antennas

The design and application of the offset wrap rib and the maypole (hoop/column) antenna configurations are described. The NASA mission model that generically categorizes the classes of user requirements, as well as the methods used to determine critical technologies and requirements are discussed. Performance estimates for the mesh deployable antenna selected for development are presented

Log-normal distribution based EMOS models for probabilistic wind speed forecasting

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN-GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.Comment: 24 pages, 10 figure

Are Core Inflation Directional Forecasts Informative?

Core inflation is under attack. Empirically, experts have become increasingly disappointed with its actual performance. Theoretically, while some claim that it is a key inflation predictor others argue that, by construction, that cannot be one of its main properties, at least in the short run. Even if true, core inflation could still be useful if it provides good directional inflation forecasts. The evidence presented here using U.S., Canadian and Brazilian data shows that this does not seem to be the case. Directional forecasts are often no better than a coin toss, especially from the level model. The gap model’s forecasts are wrong, on average, at least 20% of the time. More crucially, they are usually no better than a simple moving average of headline inflation.

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (1-R29-DC02952-01); Office of Naval Research (N00014-92-J-1309, N00014-01-1-0624); Air Force Office of Scientific Research (F49620-01-1-0397); National Institute of Neurological Disorders and Stroke (NS 33173