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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗
NASA NTRSDate not supplied

Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data

This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.

Quantum Computing↗
NASA NTRS2012

Experimenting with the GMAO 4D Data Assimilation

The Global Modeling and Assimilation Office (GMAO) has been working to promote its prototype four-dimensional variational (4DVAR) system to a version that can be exercised at operationally desirable configurations. Beyond a general circulation model (GeM) and an analysis system, traditional 4DV AR requires availability of tangent linear (TL) and adjoint (AD) models of the corresponding GeM. The GMAO prototype 4DVAR uses the finite-volume-based GEOS GeM and the Grid-point Statistical Interpolation (GSI) system for the first two, and TL and AD models derived ITom an early version of the finite-volume hydrodynamics that is scientifically equivalent to the present GEOS nonlinear GeM but computationally rather outdated. Specifically, the TL and AD models hydrodynamics uses a simple (I-dimensional) latitudinal MPI domain decomposition, which has consequent low scalability and prevents the prototype 4DV AR ITom being used in realistic applications. In the near future, GMAO will be upgrading its operational GEOS GCM (and assimilation system) to use a cubed-sphere-based hydrodynamics. This versions of the dynamics scales to thousands of processes and has led to a decision to re-derive the TL and AD models for this more modern dynamics, thus taking advantage of a two-dimensional MPI decomposition and improved scalability properties. With the aid of the Transformation of Algorithms in FORTRAN (l'AF) automatic adjoint generation tool and some hand-coding, a version of the cubed-sphere-based TL and AD models, with a simplified vertical diffusion scheme, is now available, enabling multiple configurations of standard implementations of 4DV AR in GEOS. Concurrent to this development, collaboration with the National Centers for Environmental Prediction (NCEP) and the Earth System Research Laboratory (ESRL) has allowed GMAO to implement a hybrid-ensemble capability within the GEOS data assimilation system. Both 3Dand 4D-ensemble capabilities are presently available thus allowing GMAO to now evaluate the performance and benefit of various ensemble and variational assimilation strategies. This presentation will cover the most recent developments taking place at GMAO and show results from various comparisons from traditional techniques to more recent ensemble-based ones.

Todling, R.↗
NASA NTRS1989

Large scale fabrication of lightweight Si/SiC lidar mirrors

An up-scalable CVD process has been used to fabricate 7.5-cm models of lightweight Si/SiC mirrors consisting of an f/1.6 concave face-plate of SiC coated with CVD Si, in conjunction with a lightweight backup structure of SiC. Due to CVD chamber fabrication, no bonding agent was required to attach the SiC backup structure to the face-plate. Upon up-scaling, the SiC deposition process has been able to produce 40-cm diameter f/1.6 concave mirror face-plates. The mirrors were polished to a 1/5-wave figure and 10 A rms surface finish. There appear to be no intrinsic physical limits to further up-scaling of this mirror-fabrication process.

Goela, Jitendra Singh↗
NASA NTRS2005

Biomimetic Models for An Ecological Approach to Massively-Deployed Sensor Networks

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications that will redefine the way we live and work. Due to small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors ubiquitous throughout our environment working in concert. Recent research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to parallel activities and does not scale to massive size networks. Execution of simple tasks in sparse networks will not lead to the sophisticated applications predicted. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in such a model, fully distributed data aggregation can be performed in a scalable fashion in massively deployed sensor networks, where motes operate on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. We show that such architectures may be used to facilitate communication and synchronization in a fault-tolerant manner, while balancing workload and required energy expenditure throughout the network.

Jones, Kennie H.↗
NASA NTRS2016

Design and Development of a Flight Route Modification, Logging, and Communication Network

There is an overwhelming desire to create and enhance communication mechanisms between entities that operate within the National Airspace System. Furthermore, airlines are always extremely interested in increasing the efficiency of their flights. An innovative system prototype was developed and tested that improves collaborative decision making without modifying existing infrastructure or operational procedures within the current Air Traffic Management System. This system enables collaboration between flight crew and airline dispatchers to share and assess optimized flight routes through an Internet connection. Using a sophisticated medium-fidelity flight simulation environment, a rapid-prototyping development, and a unified modeling language, the software was designed to ensure reliability and scalability for future growth and applications. Ensuring safety and security were primary design goals, therefore the software does not interact or interfere with major flight control or safety systems. The system prototype demonstrated an unprecedented use of in-flight Internet to facilitate effective communication with Airline Operations Centers, which may contribute to increased flight efficiency for airlines.

Merlino, Daniel K.↗
NASA NTRS2017

Reducing V&V Cost of Flight Critical Systems: Myth or Reality?

This paper presents an overview of NASA research program on the V&V of flight critical systems. Five years ago, NASA started an effort to reduce the cost and possibly increase the effectiveness of V&V for flight critical systems. It is the right time to take a look back and realize what progress has been made. This paper describes our overall approach and the tools introduced to address different phases of the software lifecycle. For example, we have improved testing by developing a statistical learning approach tor defining test cases. The tool automatically identifies possible unsafe conditions by analyzing outliers in output data; using an iterative learning process, it can then generate more test cases that represent potentially unsafe regions of operation. At the code level, we have developed and made available as open source a static analyzer for C and C++ programs called IKOS. We have shown that IKOS is very precise in the analysis of embedded C programs (very few false positives) and a bit less for regular C and C++ code. At the design level, in collaboration with our NRA partners, we have developed a suite of analysis tools for Simulink models. The analysis is done in a compositional framework for scalability.

Brat, Guillaume P.↗
NASA NTRSDate not supplied

A Partitioned -Task Parallel Implementation of the NASA Multiscale Analysis Tool for High Performance Computing

The NASA Multiscale Analysis Tool (NASMAT) is a “plug and play” software package that allows users to conduct massively multiscale modeling of hierarchical and nonlinear materials. This work extends the scalability and improves the High Performance Computing friendliness of NASMAT by adopting a Partitioned Task-Parallel approach. Interoperability of NASMAT with external software is enhanced through preCICE, a open source library for multiphysics coupling in a partitioned manner. Enhancement through preCICE allows for easy integration of NASMAT to other macro solvers and dissociates the parallelization strategy adopted within NASMAT from the macro solver. The task-parallel framework based on Master-Worker approach is implemented as the parallelization scheme. The scheme accounts for hierarchy of multiple scales (task-dependence) and heterogeneous nature (dynamic load balancing) of computations. The applicability and scalability of the framework will be evaluated by analyzing large scale engineering problems through massively multiscale methods.

NASMAT↗
NASA NTRS2016

Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.

SciDB↗
NASA NTRSDate not supplied

Scalability of Cohesive Fatigue Analyses Using Explicit Solvers

A cohesive fatigue law has been integrated into a constitutive material model compatible with an explicit finite element solver. The cohesive fatigue model response is based on engineering approximations of the endurance limit and the Goodman diagram. This approach can predict stress-life diagrams for crack initiation, the Paris law regime, and transient effects of crack initiation and stable tearing. Simplified cyclic loading is utilized so that the applied load(or displacement) corresponds to the peak load of a fatigue cycle. Loads are held constant during fatigue while damage develops with increasing solution increments. An automatically-calculated ratio of fatigue cycles per solution increment controls the rate of damage growth, ensuring that damage growth is modeled with a sufficient minimum number of increments and damage growth advances to a minimum desired extent within the explicit analysis step time. The compatibility with an explicit finite element solver enables the analysis of structures that are computationally intractable for implicit finite element solvers. Scalability studies are conducted for geometrically nonlinear problems involving fiber-reinforced composite structures that exhibit fatigue damage growth of interacting matrix cracks and delaminations

Frank A Leone↗
NASA NTRS2015

Overview and Accomplishments of Advanced Mirror Technology Development Phase 2 (AMTD-2) Project

The Advance Mirror Technology Development (AMTD) project is in Phase 2 of a multiyear effort, initiated in FY12, to mature by at least a half TRL step critical technologies required to enable 4 meter or larger UVOIR space telescope primary mirror assemblies for both general astrophysics and ultra-high contrast observations of exoplanets. AMTD Phase 1 completed all of its goals and accomplished all of its milestones. AMTD Phase 2 started in 2014. Key accomplishments include deriving primary mirror engineering specifications from science requirements; developing integrated modeling tools and using those tools to perform parametric design trades; and demonstrating new mirror technologies via sub-scale fabrication and test. AMTD-1 demonstrated the stacked core technique by making a 43-cm diameter 400 mm thick 'biscuit-cut' of a 4-m class mirror. AMTD-2 is demonstrating lateral scalability of the stacked core method by making a 1.5 meter 1/3rd scale model of a 4-m class mirror.

Space Telescope Mirrors↗
NASA NTRSDate not supplied

A Partitioned - Task Parallel Implementation of the NASA Multiscale Analysis Tool for High Performance Computing

The NASA Multiscale Analysis Tool (NASMAT) is a platform for multiscale modeling of composites which can perform analysis of materials with any arbitrary number of length scales. The platform supports modularity, scalability, and interoperability using recursive procedures and data structures. A Macro solver driven parallelization scheme often limits the capability of NASMAT to scale as it has access to limited memory and number of cores (often one core/thread) and often forces to implement macro solver specific changes to the platform. In this work, a partitioned task-parallel approach is adopted, where the parallelization strategy adopted for NASMAT is independent of the macro solver and the computational resources are managed independently. The programming architecture takes into account the hierarchy of multiple scales (task-dependence) and the heterogeneous nature (dynamic load balancing) of computation through implementation of a hierarchy-informed task parallel model. The partitioned nature of the framework further extends the “plug and play” capability of NASMAT. preCICE, an open-source library for coupling multiphysics solver in a partitioned manner, is adopted to integrate NASMAT with an external macro solver by implementing a NASMAT adapter for preCICE. Speedup and scalability of the framework is studied for micromechanical models of varying size.

task-parallel↗
NASA NTRSDate not supplied

Nuclear Space System Analysis and Modelling (NSSAM): A Software Tool to Efficiently Analyze the Design Space of Space Reactor Systems

Space reactors have the potential to play a key role in future NASA exploration activities due to their capability to enable sustainable power and advanced propulsion systems. To enable assessment of the space reactor design space, the nuclear space system analysis and modelling (NSSAM) software was developed by Analytical Mechanics Associates. NSSAM leverages a scalable and extensible software architecture which automates reactor analysis to perform coupled engine-reactor and reactor physics-thermal hydraulics calculations. This allows space reactor systems to be evaluated by a wider number of users with a consistent analysis approach to compare designs. NSSAM has been developed with multiple use cases to tailor the analysis to the level of detail desired by the user and computing resources. This summary overviews the NSSAM architecture and development approach, current capabilities (including design variants and use cases) and analysis approach for reactor and system component models.

nuclear thermal propulsion↗
NASA NTRS2011

MoSST DAS: The First Working Geomagnetic Data Assimilation System

The Earth possesses an internal magnetic field (geomagnetic field) generated by convection in the outer core (geodynamo). Previous efforts have been focused along two distinct paths: (1) numerical geodynamo modeling to understand the origin of the geomagnetic field, and the mechanisms of geomagnetic secular variations (SV); and (2) geomagnetic field modeling to map the spatial/temporal variations of the field from geomagnetic data, and to derive core properties, e.g. inversion of core flow near the core-mantle boundary (CMB). Geomagnetic data assimilation is a new approach emerged over the past 5 years: surface observations are assimilated with geodynamo models for better understanding of the core dynamical state, and accurately prediction of SV. In collaboration with several geomagnetic research groups, we have developed the first working geomagnetic data assimilation system, Modular, Scalable, Self-consistent, and Three-dimensional (MoSST) DAS, that includes the MoSST numerical dynamo model; 7000 years of geomagnetic field maps from several field models utilizing satellite and ground observatory data, historical magnetic records and archeo/paleo magnetic data; and an ensemble based optimal interpolation (01) assimilation algorithm. With this system, we have demonstrated clearly that the assimilated core dynamical state is substantially different from those of pure geodynamo simulations. Ensemble assimilation runs also show the convergence of the assimilated solutions inside the core, suggesting that the simulation state is pulled closer to the truth via data assimilation. The forecasts from this system are also very accurate: the 5-year forecast of the geomagnetic field agrees very well with the observations; and the 5-year secular variation forecast is more accurate than the IGRF SV forecast models in the past. Using geomagnetic records up to 2009, we have made an SV forecast for the period from 2010-2015, and is a candidate SV model for IGRF-11.

Kuang, Weijia↗
NASA NTRS1998

Implementation of a Parallel Kalman Filter for Stratospheric Chemical Tracer Assimilation

A Kalman filter for the assimilation of long-lived atmospheric chemical constituents has been developed for two-dimensional transport models on isentropic surfaces over the globe. An important attribute of the Kalman filter is that it calculates error covariances of the constituent fields using the tracer dynamics. Consequently, the current Kalman-filter assimilation is a five-dimensional problem (coordinates of two points and time), and it can only be handled on computers with large memory and high floating point speed. In this paper, an implementation of the Kalman filter for distributed-memory, message-passing parallel computers is discussed. Two approaches were studied: an operator decomposition and a covariance decomposition. The latter was found to be more scalable than the former, and it possesses the property that the dynamical model does not need to be parallelized, which is of considerable practical advantage. This code is currently used to assimilate constituent data retrieved by limb sounders on the Upper Atmosphere Research Satellite. Tests of the code examined the variance transport and observability properties. Aspects of the parallel implementation, some timing results, and a brief discussion of the physical results will be presented.

Chang, Lang-Ping↗
NASA NTRS1995

Automated Performance Prediction of Message-Passing Parallel Programs

The increasing use of massively parallel supercomputers to solve large-scale scientific problems has generated a need for tools that can predict scalability trends of applications written for these machines. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require substantial manual effort to represent an application in the model's format. The NIK toolkit described in this paper is the result of an on-going effort to automate the formation of analytic expressions of program execution time, with a minimum of programmer assistance. In this paper we demonstrate the feasibility of our approach, by extending previous work to detect and model communication patterns automatically, with and without overlapped computations. The predictions derived from these models agree, within reasonable limits, with execution times of programs measured on the Intel iPSC/860 and Paragon. Further, we demonstrate the use of MK in selecting optimal computational grain size and studying various scalability metrics.

Block, Robert J.↗
NASA NTRS2010

Freezable Radiator Model Correlation and Full Scale Design

Freezable radiators offer an attractive solution to the issue of thermal control system scalability. As thermal environments change, a freezable radiator will effectively scale the total heat rejection it is capable of as a function of the thermal environment and flow rate through the radiator. Scalable thermal control systems are a critical technology for spacecraft that will endure missions with widely varying thermal requirements. These changing requirements are a result of the space craft s surroundings and because of different thermal loads during different mission phases. However, freezing and thawing (recovering) a radiator is a process that has historically proven very difficult to predict through modeling, resulting in highly inaccurate predictions of recovery time. This paper summarizes efforts made to correlate a Thermal Desktop (TM) model with empirical testing data from two test articles. A 50-50 mixture of DowFrost HD and water is used as the working fluid. Efforts to scale this model to a full scale design, as well as efforts to characterize various thermal control fluids at low temperatures are also discussed.

Lillibridge, Sean T.↗
NASA NTRS2011

Freezable Radiator Model Correlation Improvements and Fluids Study

Freezable radiators offer an attractive solution to the issue of thermal control system scalability. As thermal environments change, a freezable radiator will effectively scale the total heat rejection it is capable of as a function of the thermal environment and flow rate through the radiator. Scalable thermal control systems are a critical technology for spacecraft that will endure missions with widely varying thermal requirements. These changing requirements are a result of the space craft s surroundings and because of different thermal rejection requirements during different mission phases. However, freezing and thawing (recovering) a radiator is a process that has historically proven very difficult to predict through modeling, resulting in highly inaccurate predictions of recovery time. To attempt to improve this, tests were conducted in 2009 to determine whether the behavior of a simple stagnating radiator could be predicted or emulated in a Thermal Desktop(trademark) numerical model. A 50-50 mixture of DowFrost HD and water was used as the working fluid. Efforts to scale this model to a full scale design, as well as efforts to characterize various thermal control fluids at low temperatures are also discussed. Previous testing and modeling efforts showed that freezable radiators could be operated as intended, and be fairly, if not perfectly predicted by numerical models. This paper documents the improvements made to the numerical model, and outcomes of fluid studies that were determined necessary to go forward with further radiator testing.

Lillibridge, Sean↗