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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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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CAMEO: A Co-design Architecture for Multi-objective Energy System Optimization (Project Report)

CAMEO (Codesign Architecture for Multi-objective Energy System Optimization) is a modular workflow management framework that abstracts co-design problems as Directed Acyclic Graphs (DAG). The framework employs JSON-based workflow specifications that enable systematic decomposition of complex optimization problems into reusable, interchangeable components including data loaders, scenario generators, optimization solvers, and result summarizers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

The CAMEO barium release - E/parallel/ fields over the polar cap

Four successive thermite barium releases at an altitude of 965 km over polar cap invariant latitudes 84 to 76 deg near magnetic midnight were conducted from the orbiting second stage of the vehicle that launched Nimbus 7; the releases were made as part of the CAMEO (Chemically Active Material Ejected in Orbit) program. This was the first opportunity to observe the behavior of conventional barium release when conducted at orbital velocity in the near-earth magnetic field. The principal unexpected characteristic in the release dynamics was the high, 1.4 to 2.6 km/s, initial Ba(+) expansion velocity relative to an expected velocity of 0.9 km/s. Attention is also given to neutral cloud expansion, initial ion cloud expansion, convective motion, and the characteristics of field-aligned motion. The possibility of measuring parallel electric fields over the polar cap by observing perturbations in the motion of the visible ions is assessed.

Heppner, J. P.↗
DOE OSTI2024

Hydrodynamic Test Requirements Process Improvements

Hydrodynamic testing at Los Alamos National Laboratory would benefit from a process improvement for the requirements process. Cameo was used as a digital solution for requirements management to allow Lead Engineers to track requirements more effectively. This was identified as a process improvement throughout this Capstone project. This report includes a project proposal, business case, literature review, methodology, project plan, data analysis, decision-making report, financial analysis report, discussion, and conclusion. Initially, this project focused on figuring out a solution for the hydrotest requirement process improvements. The scope narrowed to focus on the use of Cameo for requirement capture and management. During this Capstone, four tests had digital models produced for requirements management in Cameo. The initial model was the baseline, with core requirements used across the tests. Commonalities in tests were used and the core requirements allowed for process efficiencies. In the data analysis, it was seen that overall, the implementation of using Cameo for requirements resulted in a decreasing trend for both schedule and normalized cost. Tests have different complexity levels which is also a factor in how long the requirements process will take. Additional data is needed to continue analyzing process improvements. Through decisionmaking and financial analysis, the recommendation was to use Cameo for requirements process improvement. Multiple experts provided feedback for requirements that were then captured within models. Numerous tangible and intangible benefits were identified with this process improvement. For return on investment, the metric of success was schedule reduction, which was overall seen. Four tests were analyzed, so future analysis will be needed. There is also not a great financial risk because the main cost would be purchasing more licenses. Individuals must generate requirements whether using this software or not. Overall, training is needed to help improve the skillsets of Lead Engineers but is already being supported on a regular frequency. A desktop guide was started associated with explaining the process, however, it is a work in progress. The team plans on adding additional information in the digital models to help status when requirements are met using verification methods and artifacts. From working on this project an improved understanding of Cameo and requirements was the result. There are future opportunities to extend the usage for requirements management and progress will continue after this project.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Retrieval of Temperature and Species Distributions from Multispectral Image Data of Surface Flame Spread in Microgravity

Weight, size, and power constraints severely limit the ability of researchers to fully characterize temperature and species distributions in microgravity combustion experiments. A powerful diagnostic technique, infrared imaging spectrometry, has the potential to address the need for temperature and species distribution measurements in microgravity experiments. An infrared spectrum imaged along a line-of-sight contains information on the temperature and species distribution in the imaged path. With multiple lines-of-sight and approximate knowledge of the geometry of the combustion flowfield, a three-dimensional distribution of temperature and species can be obtained from one hyperspectral image of a flame. While infrared imaging spectrometers exist for collecting hyperspectral imagery, the remaining challenge is retrieving the temperature and species information from this data. An initial version of an infrared analysis software package, called CAMEO (Combustion Analysis Model et Optimizer), has been developed for retrieving temperature and species distributions from hyperspectral imaging data of combustion flowfields. CAMEO has been applied to the analysis of multispectral imaging data of flame spread over a PMMA surface in microgravity that was acquired in the DARTFire program. In the next section of this paper, a description of CAMEO and its operation is presented, followed by the results of the analysis of microgravity flame spread data.

Annen, K. D.↗
DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Oh, Develop

In a mature view of the subject, career development is not simply four years of college or a week at training, culminating in a diploma or a certificate to hang on an office wall. That's why we wanted to take a broad look at career development in this issue of ASK. Take for example, Dr. Gerald Mulenburg s contribution, Fly on the Wall. When Mulenburg and other members of a knowledge-sharing group at Ames were invited to observe an upcoming project review, Mulenburg thought it would be interesting to learn how another project does its reviews. Note that Mulenburg is no fresh out who's never attended a NASA project review. Not only has he been through a fair share of them as the reviewed, he has also been on the other side of the table as a reviewer. This experienced project manager recognizes that at any stage of a career there is room to grow and develop one's repertoire. Too often people associate career development with textbooks and role classroom training, far removed from project life. But classroom training need not be like this, as you'll find in our Special Feature, The Enterprise Project by Wendy Dolci, which sprung out of an APPL Advanced Project Management class in July 2003 at Ames Research Center. In addition to Dolci, some of her classmates contribute to the story. Mike Sander of the Jet Propulsion Laboratory, project manager for the Mars Science Laboratory mission, who provided the assignment on which the story is based, also has a cameo in the story. We think Dolci's story is an inspiring example of what classroom training can be if it's approached imaginatively and made to serve a practical purpose. Another story from Ames, by Frank Larsen, takes a different twist on career development. At the annual Experimental Aircraft Association Fly-in in Oshkosh, Wisconsin, Larsen represented Ames at a NASA booth. While there, Larsen met a colleague from Glenn Research Center. Months later on a project with a quick turnaround, he remembered his colleague from Glenn who had equipment that might help Larsen save time and money on his project. Although they had never worked together and they had to unravel a lot of red tape before they could collaborate, they managed a way to get the job done. There are other stories in this issue that deal directly with this theme of career development. Then there are others in which it is less explicit. But if you take the view that career development happens all the time, and is as necessary to your survival as breathing, then you can read almost any story in ASK with your career development in mind. In that case, all the best, and so then-start developing.

Post, Todd↗
DOE OSTI2023

Improving metrology with quantum scrambling

Quantum scrambling describes the spreading of information into many degrees of freedom in quantum systems, such that the information is no longer accessible locally but becomes distributed throughout the system. This idea can explain how quantum systems become classical and acquire a finite temperature, or how in black holes the information about the matter falling in is seemingly erased. We probe the exponential scrambling of a multiparticle system near a bistable point in phase space and utilize it for entanglement-enhanced metrology. A time-reversal protocol is used to observe a simultaneous exponential growth of both the metrological gain and the out-of-time-order correlator, thereby experimentally verifying the relation between quantum metrology and quantum information scrambling. Our results show that rapid scrambling dynamics capable of exponentially fast entanglement generation are useful for practical metrology, resulting in a 6.8(4)-decibel gain beyond the standard quantum limit.

Science & Technology - Other Topics↗
NASA NTRSDate not supplied

MBSE Applications for the MSR SRC Mars Ascent Vehicle

The objective of the NASA Mars Sample Return (MSR) Campaign is to collect samples from the surface of Mars and return them to Earth for scientific research. The Mars Ascent Vehicle (MAV) will be integrated into a larger Mars Sample Retrieval Lander (SRL) for transit to and storage on Mars. After all Martian samples have been collected and loaded into the MAV payload assembly, MAV will deliver the samples from the Martian surface to Mars orbit. A separate spacecraft, the Earth Return Orbiter (ERO) will retrieve the samples from Mars orbit and return them to Earth. To address common systems engineering challenges associated with using traditional systems engineering practices on complex projects, the MAV systems engineering team has explored implementation of Model-Based Systems Engineering (MBSE) tools and languages. This paper describes the current state of implementation and development of the MAV MBSE model with the Systems Modeling Language (SysML) within the scope of the MAV Systems Requirement Cycle (SRC) systems engineering workflow. The MAV MBSE model has been developed within Magic Draw – a SysML editor commonly used to implement MBSE. The MAV MBSE model has been used to develop mission phase functional flow diagrams for the Concept of Operations, decompose mission to vehicle subsystem functions, develop a functional decomposition, derive functional requirements, trace requirements up to customer-imposed requirements, trace requirements within MAV requirement space, identify requirements trace gaps, define and map the physical design space architecture, allocate requirements to subsystems, develop validation items, define assembly, integration, and test (AI&T) operations, and trace these items across driving goals to develop an integrated digital thread of systems engineering information used to drive design specifications, decision making, and ultimately design verification and validation. Findings and results associated with implementing MBSE in these ways, alongside traditional methods will be discussed.

MBSE↗
NASA NTRSDate not supplied

MBSE Applications for the MSR SRC Mars Ascent Vehicle

The objective of the NASA Mars Sample Return (MSR) Campaign is to collect samples from the surface of Mars and return them to Earth for scientific research. The Mars Ascent Vehicle (MAV) will be integrated into a larger Mars Sample Retrieval Lander (SRL) for transit to and storage on Mars. After all Martian samples have been collected and loaded into the MAV payload assembly, MAV will deliver the samples from the Martian surface to Mars orbit. A separate spacecraft, the Earth Return Orbiter (ERO) will retrieve the samples from Mars orbit and return them to Earth. To address common systems engineering challenges associated with using traditional systems engineering practices on complex projects, the MAV systems engineering team has explored implementation of Model-Based Systems Engineering (MBSE) tools and languages. This paper describes the current state of implementation and development of the MAV MBSE model with the Systems Modeling Language (SysML) within the scope of the MAV Systems Requirement Cycle (SRC) systems engineering workflow. The MAV MBSE model has been developed within Magic Draw – a SysML editor commonly used to implement MBSE. The MAV MBSE model has been used to develop mission phase functional flow diagrams for the Concept of Operations, decompose mission to vehicle subsystem functions, develop a functional decomposition, derive functional requirements, trace requirements up to customer-imposed requirements, trace requirements within MAV requirement space, identify requirements trace gaps, define and map the physical design space architecture, allocate requirements to subsystems, develop validation items, define assembly, integration, and test (AI&T) operations, and trace these items across driving goals to develop an integrated digital thread of systems engineering information used to drive design specifications, decision making, and ultimately design verification and validation. Findings and results associated with implementing MBSE in these ways, alongside traditional methods will be discussed.

MBSE↗
NASA NTRSDate not supplied

Orion SysML Model, Digital Twin, and Lessons Learned for Artemis I

In 2015 it was recognized by NASA’s Orion Chief Engineer that NASA’s design insight into the Orion subsystems for Artemis I were not sufficient to provide traditional engineering support to flight operations. To address these concerns and provide an opportunity to apply emerging model-based systems engineering and digital twin methodologies and provide opportunities for employees across NASA to get hands-on training, an Orion digital twin pilot project was initiated in 2020. With the increase in complexities of spacecraft, and decreased time to made decisions during missions in critical or emergency situations, digital modeling and integration of design can reduce the time to answer questions by days and required human resources by an order of magnitude over historical approaches and was identified to be critical capability for NASA’s future. This paper describes the genesis of this digital twin pilot project, efforts undertaken, a reproducible methodology to take available system information from a mature program to create an executable SysML model that supports a link to unit-specific data, thus forming a connection to the physical asset, and associated lessons learned and project deliverables.

MBSE↗