Influence of transient phenomena in the discharge coefficient through the intake valve in an internal combustion engine

Paper presented to the 10th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Florida, 14-16 July 2014.The project of engine intake systems involves optimization of parameters such as the pipe length and diameter, junctions, and opening and closing times of the intake and exhaust valves. The correct sizing leads to an increase of the air mass admitted to the cylinders at the desired engine operational conditions. A suitable design of the intake valves in internal combustion engines is one of the factors that maximize the amount of intake air mass to the cylinder. The parameter that determines the maximization of the mass flow through the valves is called discharge coefficient. The mass flow through the valve is usually described by the compressible flow equation through a restriction, based on a dimensional analysis of an isentropic flow. In the present work, pressure variations caused by the valve movement were investigated experimentally considering an intake system. The objective was to study and compare the dynamic response of the flow through the intake valve. For this purpose, curves of mass flow rate and the dynamic pressure in several locations of the intake system were obtained. The experimental data were obtained from the intake system connected to a cylinder head. The cylinder head was installed in an air supply system consisted by a blower, a flow measurement device, and a reservoir chamber. The valves were driven by an electric motor with controlled rotational speed. The results showed that the correct design of the intake valve affects positively the air mass flow rate.cf201

Numerical analysis of the crosswind in small solar chimney

The solar chimney (or solar updraft tower) consists of a circular solar collector, a tower in the center of the collector, and turbines installed in the collector output or the tower entrance. The solar radiation passes through the translucent collector, reaches the ground surface and heats it. The air within the device is heated by the radiation emitted by the ground and by convection currents formed under the collector. The thermal energy is stored in the absorber layer of the ground when there is incidence of solar radiation and it is released from the ground when solar radiation is low. The density difference between the hot air inside the device and the ambient air creates convection currents that drive the air in the collector from the base to the top of the tower. Finally, the airflow in the tower drives the turbines which are coupled to electrical generators. The environmental winds influence the performance of the solar updraft towers in three main ways: heat losses by convection from the outer surface of the collector to the environment, heated air drag out of the cover and drag on the top of the chimney generating a suction effect and enhancing the upward flow in the tower. This work studied the influence of crosswinds on the system flow conditions through a numerical analysis using CFD. Results indicate that an increase on the environmental crosswinds speed from 0 to 25 m/s decreased the outlet temperature of the device in 0.3% and increased the outlet velocity in 50.26%, increasing the energetic efficiency of the device in 56.31%.Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016

Instant preheating mechanism and UHECR

Top-down models assume that the still unexplained Ultra High Energy Cosmic Rays (UHECR's) are the decay products of superheavy particles. Such particles may have been produced by one of the post-inflationary reheating mechanisms and may account for a fraction of the cold dark matter. In this paper, we assess the phenomenological applicability of the simplest instant preheating framework not to describe a reheating process, but as a mechanism to generate relic supermassive particles as possible sources of UHECR's. We use cosmic ray flux and cold dark matter observational data to constrain the parameters of the model.Comment: 7 pages, 2 figures, submitted to PR

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio

Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms

Altered reinforcement learning is implicated in the causes of Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD). TS and ADHD frequently co-occur but how this affects reinforcement learning has not been investigated. We examined the ability of young people with TS (n = 18), TS+ADHD (N = 17), ADHD (n = 13) and typically developing controls (n = 20) to learn and reverse stimulus-response (S-R) associations based on positive and negative reinforcement feedback. We used a 2 (TS-yes, TS-no) x 2 (ADHD-yes, ADHD-no) factorial design to assess the effects of TS, ADHD, and their interaction on behavioural (accuracy, RT) and event-related potential (stimulus-locked P3, feedback-locked P2, feedback-related negativity, FRN) indices of learning and reversing the S-R associations. TS was associated with intact learning and reversal performance and largely typical ERP amplitudes. ADHD was associated with lower accuracy during S-R learning and impaired reversal learning (significantly reduced accuracy and a trend for smaller P3 amplitude). The results indicate that co-occurring ADHD symptoms impair reversal learning in TS+ADHD. The implications of these findings for behavioural tic therapies are discussed

DES13S2cmm: the first superluminous supernova from the Dark Energy Survey

We present DES13S2cmm, the first spectroscopically-confirmed superluminous supernova (SLSN) from the Dark Energy Survey (DES). We briefly discuss the data and search algorithm used to find this event in the first year of DES operations, and outline the spectroscopic data obtained from the European Southern Observatory (ESO) Very Large Telescope to confirm its redshift (z = 0.663 +/- 0.001 based on the host-galaxy emission lines) and likely spectral type (type I). Using this redshift, we find M_U_peak = -21.05 +0.10 -0.09 for the peak, rest-frame U-band absolute magnitude, and find DES13S2cmm to be located in a faint, low metallicity (sub-solar), low stellar-mass host galaxy (log(M/M_sun) = 9.3 +/- 0.3); consistent with what is seen for other SLSNe-I. We compare the bolometric light curve of DES13S2cmm to fourteen similarly well-observed SLSNe-I in the literature and find it possesses one of the slowest declining tails (beyond +30 days rest frame past peak), and is the faintest at peak. Moreover, we find the bolometric light curves of all SLSNe-I studied herein possess a dispersion of only 0.2-0.3 magnitudes between +25 and +30 days after peak (rest frame) depending on redshift range studied; this could be important for 'standardising' such supernovae, as is done with the more common type Ia. We fit the bolometric light curve of DES13S2cmm with two competing models for SLSNe-I - the radioactive decay of 56Ni, and a magnetar - and find that while the magnetar is formally a better fit, neither model provides a compelling match to the data. Although we are unable to conclusively differentiate between these two physical models for this particular SLSN-I, further DES observations of more SLSNe-I should break this degeneracy, especially if the light curves of SLSNe-I can be observed beyond 100 days in the rest frame of the supernova.Comment: Accepted by MNRAS (2015 January 23), 13 pages, 6 figures, 2 table

DES15E2mlf: a spectroscopically confirmed superluminous supernova that exploded 3.5 Gyr after the big bang

We present the Dark Energy Survey (DES) discovery of DES15E2mlf, the most distant superluminous supernova (SLSN) spectroscopically confirmed to date. The light curves and Gemini spectroscopy of DES15E2mlf indicate that it is a Type I superluminous supernova (SLSN-I) at z = 1.861 (a lookback time of ∼10 Gyr) and peaking at MAB = −22.3 ± 0.1 mag. Given the high redshift, our data probe the rest-frame ultraviolet (1400–3500 Å) properties of the SN, finding velocity of the C III feature changes by ∼5600 km s−1 over 14 d around maximum light. We find the host galaxy of DES15E2mlf has a stellar mass of 3.5+3.6 −2.4 × 109 M, which is more massive than the typical SLSN-I host galaxy

Superluminous supernovae from the Dark Energy Survey

We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously selected sample of SLSN events at high redshift. We present the observed g, r, i, z light curves for these SNe, which we interpolate using Gaussian processes. The resulting light curves are analysed to determine the luminosity function of SLSNe-I, and their evolutionary timescales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light-curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of<−16, suggesting that such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz