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At least 865 records · Page 48

Autonomous Maneuver Planning and Execution for GeoXO Station Keeping and Momentum Management

GOES-16 was launched in 2016 using GPS at GEO, a first for civil space. With the subsequent launch of GOES-17 in 2018, followed by GOES-18 in 2022, we have accumulated over a decade of error free GPS navigation experience at GEO. Confident in GPS performance at GEO, the next generation/NASA geosynchronous weather satellite program GeoXO will require the spacecraft flight software to automate station keeping and momentum management maneuver planning and execution. Coupled with low thrust propulsion, it gives us assurance that on-board maneuver planning and execution can be implemented at a very low risk, allowing instruments to operate through maneuvers while maintaining a more accurate orbital slot and reducing operational costs. In this paper, we discuss how GOES-R maneuver planning is currently performed on the ground and contrast this with our vision of how it might be automated on-board.

GeoXO↗

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗

Menagerie: an Automated Approach to Launch Vehicle Design

The Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC)has been performing conceptual design of launch vehicles since the Space Transportation System Program. As part of its duties ACO has been tasked with investigating the effects of known and potential threats and opportunities to a myriad of launch vehicle concepts, launch sites, and mission profiles known as the launch space. As the launch space becomes more crowded and busy with launch providers and their vehicles, the legacy method of prescribing each design discipline to a single human Subject Matter Expert (SME) has become infeasible. Rapidly changing launch, economical, and political constraints require rapid evaluation of their effects. While moving into this future of rapid response does require streamlining of data and processes, it does not however lead directly to reinventing the wheel. Flight-validated analysis tools, procedures, and lessons learned can be effectively leveraged to bring the experience from decades of government and commercial flight programs to the new paradigm. In this publication we describe the aggregation of all previous launch vehicle design efforts of ACO into an automated and integrated toolchain called menagerie. The toolchain utilizes a suite of NASA, Air Force Research Lab (AFRL), and ACO-developed tools and processes to evaluate launch vehicle concepts, mission profiles, and trades in a fraction of the time previously required.

launch vehicle↗

Supervisory Algorithm for Autonomous Hemodynamic Management Systems

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.

42 ENGINEERING↗

LDEF results for polymer matrix composite experiment AO 180

This report represents a summary of the results obtained to-date on a polymer matrix composite experiment (AO 180) located at station D-12, about 82 deg off the 'ram' direction. Different material systems comprised of graphite, boron, and aramid (Kevlar) fiber reinforcements were studied. Although previous results were presented on in-situ thermal-vacuum cycling effects, particularly dimensional changes associated with outgassing, additional comparative data will be shown from ground-based tests on control and flight samples. The system employed was fully automated for thermal-vacuum cycling using a laser interferometer for monitoring displacements. Erosion of all three classes of materials due to atomic oxygen (AO) will also be discussed, including angle of incidence effects. Data from this experiment will be compared to published results for similar materials in other LDEF experiments. Composite materials' erosion yields will be presented on an AO design nomogram useful for estimating total material loss for given exposure conditions in low Earth orbit (LEO). Optical properties of these materials will also be compared with control samples. A survey of the damage caused by micrometeoroids/debris impacts will be addressed as they relate to polymer matrix composites. Correlations between hole size and damage pattern will be given. Reference to a new nomogram for estimating the number distribution of micrometeoroid/debris impacts for a given space structure as a function of time in LEO will be addressed based on LDEF data.

Tennyson, R. C.↗

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗

Automated biowaste sampling system feces monitoring system

The Feces Monitoring System (FMS) Program designed, fabricated, assembled and tested an engineering model waste collector system (WCS) to be used in support of life science and medical experiments related to Shuttle missions. The FMS design was patterned closely after the Shuttle WCS, including: interface provisions; mounting; configuration; and operating procedures. These similarities make it possible to eventually substitute an FMS for the Shuttle WCS of Orbiter. In addition, several advanced waste collection features, including the capability of real-time inertial fecal separation and fecal mass measurement and sampling were incorporated into the FMS design.

Hunt, S. R.↗

ACTS (Advanced Communications Technology Satellite) Propagation Experiment: Preprocessing Software User's Manual

The preprocessing software manual describes the Actspp program originally developed to observe and diagnose Advanced Communications Technology Satellite (ACTS) propagation terminal/receiver problems. However, it has been quite useful for automating the preprocessing functions needed to convert the terminal output to useful attenuation estimates. Prior to having data acceptable for archival functions, the individual receiver system must be calibrated and the power level shifts caused by ranging tone modulation must be received. Actspp provides three output files: the daylog, the diurnal coefficient file, and the file that contains calibration information.

Crane, Robert K.↗

Shuttle Repair Tools Automate Vehicle Maintenance

Successfully building, flying, and maintaining the space shuttles was an immensely complex job that required a high level of detailed, precise engineering. After each shuttle landed, it entered a maintenance, repair, and overhaul (MRO) phase. Each system was thoroughly checked and tested, and worn or damaged parts replaced, before the shuttle was rolled out for its next mission. During the MRO period, workers needed to record exactly what needed replacing and why, as well as follow precise guidelines and procedures in making their repairs. That meant traceability, and with it lots of paperwork. In 2007, the number of reports generated during electrical system repairs was getting out of hand-placing among the top three systems in terms of paperwork volume. Repair specialists at Kennedy Space Center were unhappy spending so much time at a desk and so little time actually working on the shuttle. "Engineers weren't spending their time doing technical work," says Joseph Schuh, an electrical engineer at Kennedy. "Instead, they were busy with repetitive, time-consuming processes that, while important in their own right, provided a low return on time invested." The strain of such inefficiency was bad enough that slow electrical repairs jeopardized rollout on several occasions. Knowing there had to be a way to streamline operations, Kennedy asked Martin Belson, a project manager with 30 years experience as an aerospace contractor, to co-lead a team in developing software that would reduce the effort required to document shuttle repairs. The result was System Maintenance Automated Repair Tasks (SMART) software. SMART is a tool for aggregating and applying information on every aspect of repairs, from procedures and instructions to a vehicle s troubleshooting history. Drawing on that data, SMART largely automates the processes of generating repair instructions and post-repair paperwork. In the case of the space shuttle, this meant that SMART had 30 years worth of operations that it could apply to ongoing maintenance work. According to Schuh, "SMART standardized and streamlined many shuttle repair processes, saving time and money while increasing safety and the quality of repairs." Maintenance technicians and engineers now had a tool that kept them in the field, and because SMART is capable of continually evolving, each time an engineer put it to use, it would enrich the Agency-wide knowledge base. "If an engineer sees something in the work environment that they could improve, a repair process or a procedure, SMART can incorporate that data for use in future operations," says Belson.

Source record↗

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

97 MATHEMATICS AND COMPUTING↗

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗

PV Hosting Capacity Estimation: Experiences with Scalable Framework

Hosting capacity is an indication of the amount of solar photovoltaics (PV) that can be hosted in a distribution system without additional changes to infrastructure or oper-ations. This paper presents a framework for estimating the PV hosting capacity at scale. First, we analyze computational, modeling and other key challenges of performing relevant, large-scale simulations, provided along with the experiences and lessons learned. Then, we develop two open-source Python-based software tools to conduct repeatable distribution analyses: the Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations and the Job Automation and Deployment Engine (JADE) for parallelizing jobs on high-performance computing clusters. A case study of hosting capacity estimation for the SMART-DS San Francisco (SFO) 2000+ synthetic feeders, is used to demonstrate the capability of the developed DISCO+JADE framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, which can help them better plan for the overall upgrade costs to integrate more PV in the future. The experiences are shared to aid the tool users and researchers to conduct relevant studies and research.

distributed energy resources↗

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

Schuhl, Haley [Donald Danforth Plant Science Cente↗

PyARC Status Report: New Integrations and Upgrades to the Fast Reactor Analysis Workflow Management Tool

PyARC was initially developed as an open source tool to support fast reactor analyses using the Argonne Reactor Computation (ARC) code suite as a part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench initiative in FY17. The goal of this initiative is to provide a common user interface for model generation, real-time validation, execution, output processing, and visualization for all integrated codes. This is accomplished through the reliance on tools available in the Workbench framework and runtime environment. While initially developed to support the ARC codes, PyARC was extended in FY22 to wrap other NEAMS and non-ARC codes, including Griffin and OpenMC, in the supported other neutronics workflows, and support users in the adoption of NEAMS-supported high fidelity analysis codes. Most recently, NUBOW-3D, a recently adopted ARC code, was integrated to support reactor bowing calculations as well. Integration of these codes into the NEAMS Workbench directly benefits the advanced reactor modeling community by: • Providing a set of controlled, maintained, documented and validated scripts to generate inputs, which promotes best practices, reduces the learning curve, and facilitates project collaboration. • Improving the user experience: the Workbench interface provides assistance for building an input through auto-completion, real-time validation, document navigation, and geometry and results visualization. • Automating complex calculations and workflows for reactor analysis. • Helping users transition to using high-fidelity NEAMS codes along-side the ARC codes. In FY22, a progress report was published that described the state of each of the tools integrated into PyARC. Since then, there have been many enhancements and upgrades to the existing integrations as well as entirely new code integrations as well. This report details all new integrations and major developments in PyARC since the version 2.0.0 release highlighted in the FY22 report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Mossbauer-effect data-collection system

Automated data collection system which uses a small, general-purpose digital computer provides data acquisition from, and minor control of, four Mossbauer-effect experiments. This system is economical with no loss of versatility to the experimenter and is useful in handling large volumes of data from research experiments.

Aschenbrenner, R. A.↗

Thermal surveillance of volcanoes of the Cascade Range and Iceland utilizing ERTS DCP systems and imagery

The author has identified the following significant results. Significant results of the thermal surveillance of volcanoes experiment during 1972 included the design, construction, emplacement, and successful operation at volcanic sites in the Cascade Range, North America and on Surtsey, Iceland, of automated thermistor arrays which transmit ground and fumarole temperatures via the ERTS-1 data communication system to Goddard Space Flight Center. Temperature, radiance, and anomalous heat flow variations are being plotted by a U.S. Geological Survey IBM 360/65 computer program to show daily fluctuations at each of the sites. Results are being compiled in conjunction with NASA and USGS aircraft infrared survey data to provide thermal energy yield estimates during the current repose period of several Cascade Range volcanic systems. ERTS-1 MSS images have provided new information on the extent of structural elements controlling thermal emission at Lassen Volcanic National Park.

Friedman, J. D.↗

A unique exercise facility for simulating orbital extravehicular activity

A unique exercise facility has been developed and used to simulate orbital extravehicular activity (EVA). The device incorporates an arm ergometer into a mechanism which places the subject in the zero-g neutral body posture. The intent of this configuration is to elicit muscular, cardiovascular, respiratory, and thermoregulatory responses similar to those observed during orbital EVA. Experiments done with this facility will help characterize the astronaut's dynamic heat balance during EVA and will eventually lead to the development of an automated thermal control system which would more effectively maintain thermal comfort.

Williamson, Rebecca C.↗

Tools and Techniques for Evaluating the Effects of Maintenance Resource Management (MRM) in Air Safety

This research project was designed as part of a larger effort to help Human Factors (HF) implementers, and others in the aviation maintenance community, understand, evaluate, and validate the impact of Maintenance Resource Management (MRM) training programs, and other MRM interventions; on participant attitudes, opinions, behaviors, and ultimately on enhanced safety performance. It includes research and development of evaluation methodology as well as examination of psychological constructs and correlates of maintainer performance. In particular, during 2001, three issues were addressed. First a prototype process for measuring performance was developed and used. Second an automated calculator was developed to aid the HF implementer user in analyzing and evaluating local survey data. These results include being automatically compared with the experience from all MRM programs studied since 1991. Third the core survey (the Maintenance Resource Management Technical Operations Questionnaire, or 'MRM/TOQ') was further developed and tested to include topics of added relevance to the industry.

Taylor, James C.↗