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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Customized Science Carrier Modules and Accessories Developed for Microgravity Simulation Devices

Because the access to the true weightless environment of the ISS and other Low Earth Orbit (LEO) vehicles is limited and cost prohibitive, the need for ground-based microgravity simulation analogs to bridge the gravity continuum gap between 1g gravity and costly space flight studies with true weightlessness was realized. As such, the Microgravity Simulation Support Facility (MSSF) was established at NASA John F. Kennedy Space Center (KSC) to fulfill the need of the scientific community. Our facility provides the US science community with a focal point in the field of gravitational research by granting access to an array of two-dimensional(2D) and three-dimensional (3D)clinostats, and Random Positioning Machine (RPM) platforms developed by teams throughout the world. The availability of these capabilities will allow further understanding the role of altered gravity vectors from “functional weightlessness”, partial gravity conditions (1 x 10-3to 0.9g, and Moon and Mars ‘g vectors), to hypergravity (up to3g) on biological processes. We have designed and 3D printed a variety of specialized attachments(and associated “static control” hardware)to allow researchers to mount their samples onto these microgravity simulation devices to meet their unique science requirements. A summary of each attachment module will be presented below. Note: All software or code information should be requested to the author Ye Zhang, Kennedy Space Center ye.zhang-1@nasa.gov

Ye Zhang↗
NASA NTRSDate not supplied

Goes-16 On-Station Custom Maneuver Generation with Focussuite

This paper discusses the process of the NOAA GOES Mission Operations Support Team (MOST) in mitigating the effects of inconsistently performing ArcJet Thrusters (AJTs) aboard the GOES-16 spacecraft in geostationary orbit. These AJTs are hot-gas thrusters which augment the gas flow using an electric arc to increase the exit velocity of the gases. Fuel flow problems have led to GOES-16 being unable to safely use its AJTs for maneuvers at times. This primarily affects North/South Station-Keeping (NSSK) maneuvers, which control inclination and use a majority of the station-keeping fuel budget. As a result, GOES-16 has had to occasionally resort to using non-AJT hot-gas thrusters. These thrusters are not placed as close to GOES-16's centerline, have lower thrust, and have lower specific impulse when compared to AJTs. In addition, MOST’s ground systems do not officially support using these non-AJT thrusters for NSSK maneuvers. MOST had to quickly develop workaround solutions using the FocusSuite Autofocus programming environment to compute the maneuvers. This paper outlines the mixture of commercial off-the-shelf (COTS) and MOST-developed products which have allowed GOES-16 to continue station-keeping even with inconsistently-per-forming AJTs, showing that script-based automation systems like Autofocus can simplify complex solutions to a few scripted user inputs.

Henry Heim↗
NASA NTRS2022

Wallops Flight Facility Integration and Testing Customer Handbook

The Wallops Integration and Testing Handbook is a guide for planning operations at Wallops testing facilities. It provides a summary of the capabilities and technical specifications of available facilities. This handbook prescribes the information to be provided by the user that will enable the integration and testing team to effectively plan for and support the facility user’s project. Note that each facility plans for availability based on current workloads/ mission schedules. For example, sounding rockets may have between 20 and 35 missions in work at one time. Also note that special training, such as cleanroom certification, electrostatic discharge (ESD) or confined space training may be required for entrance to some facilities.

Adam Hammoudi↗
NASA NTRS2024

Runtime Thread-Block Optimization for Custom Multistream CUDA Kernels for the Glenn Research Center Communication Analysis Suite

In preparation of the return of humans to the Moon with the coming Artemis missions, NASA scientists must evaluate proposed landing site locations for terrain and communications viability. The Glenn Research Center Communication Analysis Suite (GCAS) combines sophisticated communication network models with accurate lunar terrain to access sites across the Moon’s south pole. Given the importance of proper site selection to crew safety and mission success, many locations need to be analyzed resulting in a large computational load needing to be performed. To meet the growing project demands, development has begun to improve the runtime efficiency of GCAS with GPU parallelization by way of multi-stream CUDA kernels. One of the most prominent factors in kernel optimization is the proper selection of thread-block dimensions in order to maximize the concurrent operation on the device. Typically, thread-block dimensions are optimized by hand requiring many stages of benchmarking and iteration. Additionally, given the main conditions to optimization are the physical GPU architecture and problem size, these optimal dimensions are non-portable and fragile in their scope. As such, a novel optimization routine was developed to generate the optimal thread-block dimensions during runtime with considerations to hardware specifications and problem size resolving the issues of portability and enabling the function of more dynamic routines.

Aden Bergstresser↗
DOE OSTI2017

Quality Assurance on a Custom SiPMs Array for the Mu2e Experiment

The Mu2e experiment at Fermilab will search for the coherent $\mu \to e$ conversion on aluminum atoms. The detector system consists of a straw tube tracker and a crystal calorimeter. A pre-production of 150 Silicon Photomultiplier arrays for the Mu2e calorimeter has been procured. A detailed quality assur- ance has been carried out on each SiPM for the determination of its own operation voltage, gain, dark current and PDE. The measurement of the mean-time-to-failure for a small random sample of the pro-production group has been also completed as well as the determination of the dark current increase as a function of the ioninizing and non-ioninizing dose.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Distributed Energy Resources Customer Adoption Model - Graphical User Interface, Version 2.1.8

The DER-CAM Graphical User Interface has been redesigned to consist of a dynamic tree structure on the left side of the application window to allow users to quickly navigate between different data categories and views. Views can either be tables with model parameters and input data, the optimization results, or a graphical interface to draw circuit topology and visualize investment results. The model parameters and input data consist of tables where values are assigned to specific keys. The aggregation of all model parameters and input data amounts to the data required to build a DER-CAM model, and is passed to the GAMS solver when users initiate the DER-CAM optimization process. Passing data to the GAMS solver relies on the use of a Java server that handles DER-CAM requests, queuing, and results delivery. This component of the DER-CAM GUI can be deployed either locally or remotely, and constitutes an intermediate step between the user data input and manipulation, and the execution of a DER-CAM optimization in the GAMS engine. The results view shows the results of the DER-CAM optimization and distinguishes between a single and a multi-objective process. The single optimization runs the DER-CAM optimization once and presents the results as a combination of summary charts and hourly dispatch profiles. The multi-objective optimization process consists of a sequence of runs initiated by the GUI, including: 1) CO2 minimization, 2) cost minimization, 3) a user defined number of points in-between objectives 1) and 2). The multi-objective results view includes both access to the detailed results of each point generated by the process as well as the generation of a Pareto Frontier graph to illustrate the trade-off between objectives. DER-CAM GUI 2.1.8 also introduces the ability to graphically generate circuit topologies, enabling support to DER-CAM 5.0.0. This feature consists of: 1) The drawing area, where users can manually create nodes and define their properties (e.g. point of common coupling, slack bus, load) and connect them through edges representing either power lines, transformers, or heat pipes, all with user defined characteristics (e.g., length, ampacity, inductance, or heat loss); 2) The tables, which display the user-defined topology in the final numerical form that will be passed to the DER-CAM optimization. Finally, the DER-CAM GUI is also deployed with a database schema that allows users to provide different energy load profiles, solar irradiance profiles, and tariff data, that can be stored locally and later used in any DER-CAM model. However, no real data will be delivered with this version.

Ewald, Friedrich↗