Advanced Transport Operating System (ATOPS) Flight Management/Flight Controls (FM/FC) software description

The flight software developed for the Flight Management/Flight Controls (FM/FC) MicroVAX computer used on the Transport Systems Research Vehicle for Advanced Transport Operating Systems (ATOPS) research is described. The FM/FC software computes navigation position estimates, guidance commands, and those commands issued to the control surfaces to direct the aircraft in flight. Various modes of flight are provided for, ranging from computer assisted manual modes to fully automatic modes including automatic landing. A high-level system overview as well as a description of each software module comprising the system is provided. Digital systems diagrams are included for each major flight control component and selected flight management functions

The LVLASO I/O Concentrator Software Description

This paper describes the software written for the VO Concentrator Unit in support of the Low Visibility Landing and Surface Operations (LVLASO) experiment flown on-board NASA's Boeing 757 aircraft

Advanced transport operating system software upgrade: Flight management/flight controls software description

The Flight Management/Flight Controls (FM/FC) software for the Norden 2 (PDP-11/70M) computer installed on the NASA 737 aircraft is described. The software computes the navigation position estimates, guidance commands, those commands to be issued to the control surfaces to direct the aircraft in flight based on the modes selected on the Advanced Guidance Control System (AGSC) mode panel, and the flight path selected via the Navigation Control/Display Unit (NCDU)

Advanced Transport Operating System (ATOPS) utility library software description

The individual software processes used in the flight computers on-board the Advanced Transport Operating System (ATOPS) aircraft have many common functional elements. A library of commonly used software modules was created for general uses among the processes. The library includes modules for mathematical computations, data formatting, system database interfacing, and condition handling. The modules available in the library and their associated calling requirements are described

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet's use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters.Comment: 9 pages, under publication at KDD'1

Solar thermochemical water splitting: Advances in materials and methods

Photoelectrochemical (PEC) water splitting, termed artificial photosynthesis, converts solar energy into hydrogen by harvesting a narrow spectrum of visible light using photovoltaics integrated with water-splitting electrocatalysts. While conceptually attractive, critical materials issues currently challenge technology development(1) and economic viability(2). Despite decades of active research, this approach has not been demonstrated at power levels above a few watts, or for more than a few days of operation. High-temperature solar thermochemical (STCH) water splitting is an alternative approach that converts solar energy into hydrogen by using the deceptively simple metal oxide-based thermochemical cycle presented in figure 1. The STCH process requires very high temperatures, achieved by collecting and concentrating solar energy. Unlike PEC, two-step metal oxide water-splitting cycles have been demonstrated at the 100kW scale(3), and continuous operation at even higher power levels is nearing pre-commercial demonstration (HYDROSOL-3D). Nonetheless STCH, like PEC, faces critical materials issues that must be addressed for this technology to achieve commercial success. Please click Additional Files below to see the full abstract

Concerted Rattling in CsAg5Te3 Leading to Ultralow Thermal Conductivity and High Thermoelectric Performance

Thermoelectric (TE) materials convert heat energy directly into electricity, and introducing new materials with high conversion efficiency is a great challenge because of the rare combination of interdependent electrical and thermal transport properties required to be present in a single material. The TE efficiency is defined by the figure of merit ZT=(S2Ï ) T/κ, where S is the Seebeck coefficient, Ï is the electrical conductivity, κ is the total thermal conductivity, and T is the absolute temperature. A new pâ type thermoelectric material, CsAg5Te3, is presented that exhibits ultralow lattice thermal conductivity (ca. 0.18â Wmâ 1â Kâ 1) and a high figure of merit of about 1.5 at 727â K. The lattice thermal conductivity is the lowest among stateâ ofâ theâ art thermoelectrics; it is attributed to a previously unrecognized phonon scattering mechanism that involves the concerted rattling of a group of Ag ions that strongly raises the Grüneisen parameters of the material.A pâ type thermoelectric material, CsAg5Te3, is presented. It exhibits ultralow thermal conductivity (Ï°tolâ 0.18â Wmâ 1â Kâ 1) and a high figure of merit (ZTâ 1.5 at 727â K). The low thermal conductivity is attributed to a previously unrecognized phonon scattering mechanism that involves the rattling of Ag ions, strongly raising the Grüneisen parameters of the material.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134211/1/anie201605015_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134211/2/anie201605015.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134211/3/anie201605015-sup-0001-misc_information.pd