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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Utilizing Testing Frameworks for Launch Control Systems Continuous Integration

Command and control software is an integral part of the launch procedure. The most important part of this type of software is its ability to communicate well with the user and relay information in a correctly formatted way such that the user can understand the data. There is a tool that aides the communication between the different parts of the system, and effectively, the user. This instrument is capable of taking several complex values and ensuring that they are correctly sorted into their distinctive message values and distributed properly among the different facets of the system. This tool will easily translate and publish the data inside of messages in the system to something that is readable and understandable. The tool also allows for transmission of the recorded data to the user, effectively ensuring the communication between different components of the system. As well as keeping track of messages and ensuring that the information contained within each of them reaches the correct location, this tool has the ability to keep track of its own statistics and determine how many messages passed in were erroneous and how many were successfully transmitted. It is able to check and see what the total message failure count is when an invalid message is given, as well as the number of different messages and their respective types passed into the tool. This tool is of great value to the new Space Launch System (SLS). As such, the tool must be thoroughly tested with test cases that, although improbable, are possible, where the tool may not function properly. Testing an interface this complex is necessary to ensure mission safety and create unlikely scenarios where the tool would work as intended, and stretch its limits to test that even under the most uncommon conditions it would still continue to function. This software will be an important part of the control system for the newest spacecraft which will fly deeper into space than humans have ever travelled. It will fly beyond the moon, into deep space to Mars and perhaps set the groundwork for a manned mission even further to create more opportunities for interplanetary and even interstellar travel by humans. This mission relies heavily on software and hardware to ensure the safety of the humans that will be on board and therefore must be checked, exhausting each and every different situation, such that there is not a doubt surrounding the well-being of the humans aboard the rocket. That is why testing is such an important part of the mission. It provides evidence that the systems aboard the rocket and on the launch pad are safe.

Unit Testing↗
DOE OSTI2017

CIVET: Continuous Integration, Verification, Enhancement, and Testing

A Git server (GitHub, GitLab, BitBucket) sends event notifications to the Civet server. These are either a " Pull Request" or a "Push" notification. Civet then checks the database to determine what tests need to be run and marks them as ready to run. Civet clients, running on dedicated machines, query the server for available jobs that are ready to run. When a client gets a job it executes the scripts attached to the job and report back to the server the output and exit status. When the client updates the server, the server will also update the Git server with the result of the job, as well as updating the main web page.

Alger, Brian↗
DOE OSTI2022

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

Engineering↗
DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Continuous-Integration Laser Energy Lidar Monitor

This circuit design implements an integrator intended to allow digitization of the energy output of a pulsed laser, or the energy of a received pulse of laser light. It integrates the output of a detector upon which the laser light is incident. The integration is performed constantly, either by means of an active integrator, or by passive components.

Karsh, Jeremy↗
DOE OSTI2016

CIVET: Continuous Integration, Verification, Enhancement, and Testing

A Git server (GitHub, GitLab, BitBucket) sends event notifications to the Civet server. These are either a " Pull Request" or a "Push" notification. Civet then checks the database to determine what tests need to be run and marks them as ready to run. Civet clients, running on dedicated machines, query the server for available jobs that are ready to run. When a client gets a job it executes the scripts attached to the job and report back to the server the output and exit status. When the client updates the server, the server will also update the Git server with the result of the job, as well as updating the main web page.

Alger, Brian↗
DOE OSTI2023

Controls at the Fermilab PIP-II Superconducting Linac

PIP-II is an 800 MeV superconducting RF linac under development at Fermilab. As the new first stage in our accelerator chain, it will deliver high-power beam to multiple experiments simultaneously and thus drive Fermilab’s particle physics program for years to come. In a pivot for Fermilab, controls for PIP-II are based on EPICS instead of ACNET, the legacy control system for accelerators at the lab. This paper discusses the status of the EPICS controls work for PIP-II. We describe the EPICS tools selected for our system and the experience of operators new to EPICS. We introduce our continuous integration / continuous development environment. We also describe some efforts at cooperation between EPICS and ACNET, or efforts to move towards a unified interface that can apply to both control systems.

43 PARTICLE ACCELERATORS↗