Combustion control of the Homogenous Charge Compression Ignition dynamics

The HCCI engine has potential to replace the spark ignition and compression ignition engines of today. One of the main problems in making the engine commercially attractive is that there are no direct means of controlling the ignition phasing. This thesis attempts to describe a method for system identification of the HCCI process, and development of an effective LQG regulator for the combustion process. Matlab and Simulink are used in computations and simulations

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

Dynamic Objects Segmentation for Visual Localization in Urban Environments

Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects. Consequently, visual odometry pipelines often diverge and the localization systems malfunction as detected features are not consistent with the precomputed 3D model. In this work, we present an approach to automatically detect dynamic object instances to improve the robustness of vision-based localization and mapping in crowded environments. By training a convolutional neural network model with a combination of synthetic and real-world data, dynamic object instance masks are learned in a semi-supervised way. The real-world data can be collected with a standard camera and requires minimal further post-processing. Our experiments show that a wide range of dynamic objects can be reliably detected using the presented method. Promising performance is demonstrated on our own and also publicly available datasets, which also shows the generalization capabilities of this approach.Comment: 4 pages, submitted to the IROS 2018 Workshop "From Freezing to Jostling Robots: Current Challenges and New Paradigms for Safe Robot Navigation in Dense Crowds

Unequal mass binary neutron star simulations with neutrino transport: Ejecta and neutrino emission

We present 12 new simulations of unequal mass neutron star mergers. The simulations are performed with the SpEC code, and utilize nuclear-theory-based equations of state and a two-moment gray neutrino transport scheme with an improved energy estimate based on evolving the number density. We model the neutron stars with the SFHo, LS220, and DD2 equations of state (EOS) and we study the neutrino and matter emission of all 12 models to search for robust trends between binary parameters and emission characteristics. We find that the total mass of the dynamical ejecta exceeds 0.01  M⊙ only for SFHo with weak dependence on the mass ratio across all models. We find that the ejecta have a broad electron fraction (Y_e) distribution (≈0.06–0.48), with mean 0.2. Y_e increases with neutrino irradiation over time, but decreases with increasing binary asymmetry. We also find that the models have ejecta with a broad asymptotic velocity distribution (≈0.05–0.7c). The average velocity lies in the range 0.2c−0.3c and decreases with binary asymmetry. Furthermore, we find that disk mass increases with binary asymmetry and stiffness of the EOS. The Y_e of the disk increases with softness of the EOS. The strongest neutrino emission occurs for the models with soft EOS. For (anti) electron neutrinos we find no significant dependence of the magnitude or angular distribution or neutrino luminosity with mass ratio. The heavier neutrino species have a luminosity dependence on mass ratio but an angular distribution which does not change with mass ratio

Noncontact atomic force microscopy simulator with phase-locked-loop controlled frequency detection and excitation

A simulation of an atomic force microscope operating in the constant amplitude dynamic mode is described. The implementation mimics the electronics of a real setup including a digital phase-locked loop (PLL). The PLL is not only used as a very sensitive frequency detector, but also to generate the time-dependent phase shifted signal driving the cantilever. The optimum adjustments of individual functional blocks and their joint performance in typical experiments are determined in detail. Prior to testing the complete setup, the performances of the numerical PLL and of the amplitude controller were ascertained to be satisfactory compared to those of the real components. Attention is also focused on the issue of apparent dissipation, that is, of spurious variations in the driving amplitude caused by the nonlinear interaction occurring between the tip and the surface and by the finite response times of the various controllers. To do so, an estimate of the minimum dissipated energy that is detectable by the instrument upon operating conditions is given. This allows us to discuss the relevance of apparent dissipation that can be conditionally generated with the simulator in comparison to values reported experimentally. The analysis emphasizes that apparent dissipation can contribute to the measured dissipation up to 15% of the intrinsic dissipated energy of the cantilever interacting with the surface, but can be made negligible when properly adjusting the controllers, the PLL gains and the scan speed. It is inferred that the experimental values of dissipation usually reported in the literature cannot only originate in apparent dissipation, which favors the hypothesis of "physical" channels of dissipation

Low mass binary neutron star mergers : gravitational waves and neutrino emission

Neutron star mergers are among the most promising sources of gravitational waves for advanced ground-based detectors. These mergers are also expected to power bright electromagnetic signals, in the form of short gamma-ray bursts, infrared/optical transients, and radio emission. Simulations of these mergers with fully general relativistic codes are critical to understand the merger and post-merger gravitational wave signals and their neutrinos and electromagnetic counterparts. In this paper, we employ the SpEC code to simulate the merger of low-mass neutron star binaries (two $1.2M_\odot$ neutron stars) for a set of three nuclear-theory based, finite temperature equations of state. We show that the frequency peaks of the post-merger gravitational wave signal are in good agreement with predictions obtained from simulations using a simpler treatment of gravity. We find, however, that only the fundamental mode of the remnant is excited for long periods of time: emission at the secondary peaks is damped on a millisecond timescale in the simulated binaries. For such low-mass systems, the remnant is a massive neutron star which, depending on the equation of state, is either permanently stable or long-lived. We observe strong excitations of l=2, m=2 modes, both in the massive neutron star and in the form of hot, shocked tidal arms in the surrounding accretion torus. We estimate the neutrino emission of the remnant using a neutrino leakage scheme and, in one case, compare these results with a gray two-moment neutrino transport scheme. We confirm the complex geometry of the neutrino emission, also observed in previous simulations with neutrino leakage, and show explicitly the presence of important differences in the neutrino luminosity, disk composition, and outflow properties between the neutrino leakage and transport schemes.Comment: Accepted by PRD; 23 pages; 24 figures; 4 table

Neutron star-black hole mergers with a nuclear equation of state and neutrino cooling: Dependence in the binary parameters

We present a first exploration of the results of neutron star-black hole mergers using black hole masses in the most likely range of $7M_\odot-10M_\odot$, a neutrino leakage scheme, and a modeling of the neutron star material through a finite-temperature nuclear-theory based equation of state. In the range of black hole spins in which the neutron star is tidally disrupted ($\chi_{\rm BH}\gtrsim 0.7$), we show that the merger consistently produces large amounts of cool ($T\lesssim 1\,{\rm MeV}$), unbound, neutron-rich material ($M_{\rm ej}\sim 0.05M_\odot-0.20M_\odot$). A comparable amount of bound matter is initially divided between a hot disk ($T_{\rm max}\sim 15\,{\rm MeV}$) with typical neutrino luminosity $L_\nu\sim 10^{53}\,{\rm erg/s}$, and a cooler tidal tail. After a short period of rapid protonization of the disk lasting $\sim 10\,{\rm ms}$, the accretion disk cools down under the combined effects of the fall-back of cool material from the tail, continued accretion of the hottest material onto the black hole, and neutrino emission. As the temperature decreases, the disk progressively becomes more neutron-rich, with dimmer neutrino emission. This cooling process should stop once the viscous heating in the disk (not included in our simulations) balances the cooling. These mergers of neutron star-black hole binaries with black hole masses $M_{\rm BH}\sim 7M_\odot-10M_\odot$ and black hole spins high enough for the neutron star to disrupt provide promising candidates for the production of short gamma-ray bursts, of bright infrared post-merger signals due to the radioactive decay of unbound material, and of large amounts of r-process nuclei.Comment: 20 pages, 19 figure

Binary Neutron Stars with Arbitrary Spins in Numerical Relativity

We present a code to construct initial data for binary neutron star systems in which the stars are rotating. Our code, based on a formalism developed by Tichy, allows for arbitrary rotation axes of the neutron stars and is able to achieve rotation rates near rotational breakup. We compute the neutron star angular momentum through quasi-local angular momentum integrals. When constructing irrotational binary neutron stars, we find a very small residual dimensionless spin of $\sim 2\times 10^{-4}$. Evolutions of rotating neutron star binaries show that the magnitude of the stars' angular momentum is conserved, and that the spin- and orbit-precession of the stars is well described by post-Newtonian approximation. We demonstrate that orbital eccentricity of the binary neutron stars can be controlled to $\sim 0.1\%$. The neutron stars show quasi-normal mode oscillations at an amplitude which increases with the rotation rate of the stars.Comment: 20 pages, 22 figure