Asymptotic solutions of glass temperature profiles during steady optical fibre drawing

In this paper we derive realistic simplified models for the high-speed drawing of glass optical fibres via the downdraw method, that capture the fluid dynamics and heat transport in the fibre via conduction, convection and radiative heating. We exploit the small aspect ratio of the fibre and the relative orders of magnitude of the dimensionless parameters that characterize the heat transfer to reduce the problem to one- or two-dimensional systems via asymptotic analysis. The resulting equations may be readily solved numerically and in many cases admit exact analytic solutions. The systematic asymptotic breakdown presented is used to elucidate the relative importance of furnace temperature profile, convection, surface radiation and conduction in each portion of the furnace and the role of each in controlling the glass temperature.\ud \ud The models derived predict many of the qualitative features observed in the real industrial process, such as the glass temperature profile within the furnace and the sharp transition in fibre thickness. The models thus offer a desirable route to quick scenario testing, providing valuable practical information into the dependencies of the solution on the parameters and the dominant heat-transport mechanism

White Dwarf Cosmochronology in the Solar Neighborhood

The study of the stellar formation history in the solar neighborhood is a powerful technique to recover information about the early stages and evolution of the Milky Way. We present a new method which consists of directly probing the formation history from the nearby stellar remnants. We rely on the volume complete sample of white dwarfs within 20 pc, where accurate cooling ages and masses have been determined. The well characterized initial-final mass relation is employed in order to recover the initial masses (1 < M/Msun < 8) and total ages for the local degenerate sample. We correct for moderate biases that are necessary to transform our results to a global stellar formation rate, which can be compared to similar studies based on the properties of main-sequence stars in the solar neighborhood. Our method provides precise formation rates for all ages except in very recent times, and the results suggest an enhanced formation rate for the solar neighborhood in the last 5 Gyr compared to the range 5 < Age (Gyr) < 10. Furthermore, the observed total age of ~10 Gyr for the oldest white dwarfs in the local sample is consistent with the early seminal studies that have determined the age of the Galactic disk from stellar remnants. The main shortcoming of our study is the small size of the local white dwarf sample. However, the presented technique can be applied to larger samples in the future.Comment: 25 pages, 10 figures, accepted for publication in the Astrophysical Journa

The feasibility of a programmed heat shield for solar cell performance control

Feasibility of programmed heat shield for temperature and power control for spacecraft on-board powe

Implementing knowledge building: analysis of a face to face discussion by grade four students

Researchers say that teachers can implement an educational innovation without adhering to the principles underpinning its design. Such principles may not adequately take typical classroom conditions into account. The goal of this study was to explore tensions between attempts to implement the principles underpinning knowledge building and the influence of contextual factors that compete for the teacher's attention. To this end, we discuss five excerpts from a discussion of the motion of spinning tops held by a class of Grade-4 students, coming at the end of a five-month implementation of knowledge building. Each excerpt is followed first by the teacher's perspective and then by the researcher's perspective. Our analysis highlights two tensions that constrain agency, arising from the students' need for social development and their need to learn scientific concepts. We offer some suggestions for addressing these tensions.postprintThis work was supported by a grant from the Social Sciences and Humanities Research Council of Canada to the first author (Grant 410-2000-0998)

Operator Scheduling Strategies in Supervisory Control of Multiple UAVs

The application of network centric operations to time-constrained command and control environments will mean that human operators will be increasingly responsible for multiple simultaneous supervisory control tasks. One such futuristic application will be the control of multiple unmanned aerial vehicles (UAVs) by a single operator. To achieve such performance in complex, time critical, and high risk settings, automated systems will be required both to guarantee rapid system response as well as manageable workload for operators. Through the development of a simulation test bed for human supervisory control of multiple independent UAVs by a single operator, this paper presents recent efforts to investigate workload mitigation strategies as a function of increasing automation. A humanin- the-loop experiment revealed that under low workload conditions, operators’ cognitive strategies were relatively robust across increasing levels of automated decision support. However, when provided with explicit automated recommendations and with the ability to negotiate with external agencies for delays in arrival times for targets, operators inappropriately fixated on the need to globally optimize their schedules. In addition, without explicit visual representation of uncertainty, operators tended to treated all probabilities uniformly. This study also revealed that operators that reached cognitive saturation adapted two very distinct management strategies, which led to varying degrees of success. Lastly, operators with management-by-exception decision support exhibited evidence of automation bias.This research was sponsored by Boeing Phantom Works

A Predictive Model for Human-Unmanned Vehicle Systems : Final Report

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles (UVs). This capability is desirable in order to reduce the operational costs of human-UV systems (HUVS), extend human capabilities, and improve system effectiveness. However, the high complexity of these systems introduces many significant challenges to system designers. To help understand and overcome these challenges, high-fidelity computational models of the HUVS must be developed. These models should have two capabilities. First, they must be able to describe the behavior of the various entities in the team, including both the human operator and the UVs in the team. Second, these models must have the ability to predict how changes in the HUVS and its mission will alter the performance characteristics of the system. In this report, we describe our work toward developing such a model. Via user studies, we show that our model has the ability to describe the behavior of a HUVS consisting of a single human operator and multiple independent UVs with homogeneous capabilities. We also evaluate the model’s ability to predict how changes in the team size, the human-UV interface, the UV’s autonomy levels, and operator strategies affect the system’s performance.Prepared for MIT Lincoln Laborator

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

In recent years, much research has focused on making possible single operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes. We assess the ability of this set of metric classes to (a) predict the number of robots that should be in the team and (b) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.This research was funded by MIT Lincoln Laboratory