Photogrammetry-Based Analysis of the On-Orbit Structural Dynamics of the Roll-Out Solar Array

The Roll-Out Solar Array (ROSA) flight experiment was launched to the International Space Station (ISS) on June 3rd, 2017. ROSA is an innovative, lightweight solar array with a flexible substrate that makes use of the stored strain energy in its composite structural members to provide deployment without the use of motors. This paper will discuss the results of various structural dynamics experiments conducted on the ISS during the weeks following launch. Data gathered from instrumentation on the solar array wing during the experiments was previously compared with pre-flight predictions from two different Finite Element Modeling (FEM) efforts. In this paper, data generated from photogrammetry is compared with accelerometer data and used to extend previous conclusions. Whereas previous analyses were only able to track the accelerations of six discrete points on the structure and photovoltaic (PV) blanket of ROSA, the photogrammetry analysis makes available displacements for dozens of points distributed throughout the array. This larger data set makes it possible to compare higher-order PV blanket modes with FEM predictions, in addition to verifying conclusions reached using accelerometer data. The goal in this effort was to better understand the performance of ROSA and to improve modeling efforts for future designs of similar solar arrays

Structural Analysis Methods for the Roll-Out Solar Array Flight Experiment

The Roll-Out Solar Array (ROSA) flight experiment was launched to the International Space Station (ISS) on June 3rd, 2017. ROSA is an innovative, lightweight solar array with a flexible substrate that makes use of the stored strain energy in its composite structural members to provide deployment without the use of motors. This paper discusses the effort to model the structural dynamics of ROSA using finite element modeling. Two distinct and agnostic approaches were used by separate teams to assess the structural dynamics of the solar array prior to ground vibrational testing and flight testing. Results from each approach are compared to measured dynamics from accelerometers and photogrammetry data gathered on orbit. Advantages and disadvantages of each approach are discussed as are preliminary efforts to calibrate the models to the empirical data for the benefit of future modeling efforts on similar space structures

LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5-89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions

Investigation of reactivity of launch vehicle materials with liquid oxygen Quarterly report, 23 Jul. - 22 Oct. 1968

Reactivity of launch vehicle organic materials with liquid oxyge

Investigation of Reactivity of Launch Vehicle Materials with Liquid Oxygen

Impact sensitivity and ignition mechanism of organic compounds in liquid oxygen correlated with chemical and physical propertie

On-Orbit Structural Dynamics Performance of the Roll-Out Solar Array

The Roll-Out Solar Array (ROSA) flight experiment was launched to the International Space Station (ISS) on June 3rd, 2017. ROSA is an innovative, lightweight solar array with a flexible substrate that makes use of the stored strain energy in its composite structural members to provide deployment without the use of motors. This paper will discuss the results of various structural dynamics experiments conducted on the ISS during the weeks following launch. Data gathered from instrumentation on the solar array wing during the experiments are compared with pre-flight predictions from two different finite element modeling efforts. Two distinct methods were used to reconstruct the modal characteristics of ROSA from the data collected on orbit. Of particular interest in this effort are the first few system modes and mode shapes of the array, the amount of structural damping present, and degree of structural thermal interaction seen during eclipse exit. Discrepancies between the behavior predicted by the models and that observed on orbit are identified and discussed. The goal in this effort was to better understand the performance of ROSA and to improve modeling efforts for future designs of similar solar arrays

Statistics on Logic Simulation

The high costs associated with logic simulation of large VLSI based systems have led to the need for new computer architectures tailored to the simulation task. Such architecture have the potential for significant speedups over standard software based logic simulators. Several commercial simulation engines have been produced to satisfy need in this area. To properly explore the space of alternative simulation architectures, data is required on the simulation process itself. This paper presents a framework for such data gathering activity by first examining possible sources of speedup in the logic simulation task, examining the sort of data needed in the design of simulation engines, and then presenting such data. The data contained in the paper includes information on the subtask times found in standard discrete event simulation algorithms, event intensities, queue length distributions and simultaneous event distributions

The Mercury System: Exploiting Truly Fast Hardware in Data Mining

In many data mining applications, the size of the database is not only extremely large, it is also growing rapidly. Even for relatively simple searches, the time required to move the data off magnetic media, cross the system bus into main memory, copy into processor cache, and then execute code to perform a search is prohibitive. We are building a system in which a significant portion of the data mining task (i.e., the portion that examines the bulk of the raw data) is implemented in fast hardware, close to the magnetic media on which it is stored. Furthermore, this hardware can be replicated allowing mining tasks to be performed in parallel, thus providing further speedup for the overall mining application. In this paper, we describe a general framework under which this can be accomplished and provide initial performance results for a set of applications