Integrating Model-based Artificial Intelligence Planning with Procedural Elaboration for Onboard Spacecraft Autonomy
This paper describes the integration of a model-based planner into a procedural architecture.
planning scheduling architecture↗
SEARCH · Search NASA
Results for “model integration”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Local parallel models for integration of stereo matching constraints and intrinsic image combination
Parallel relaxation computations such as those of connectionist networks offer a useful model for constraint integration and intrinsic image combination in developing a general-purpose stereo matching algorithm. This paper describes such a stereo algorithm that incorporates hierarchical, surface-structure, and edge-appearance constraints that are redefined and integrated at the level of individual candidate matches. The algorithm produces a high percentage of correct decisions on a wide variety of stereo pairs. Its few errors arise when the correlation measures defined by the constraints are either weakened or ambiguous, as in the case of periodic patterns in the images. Two additional mechanisms are discussed for overcoming the remaining errors.
Stewart, Charles V.↗
Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation
This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.
Miller, David W.↗
ED1: Design and Prototype of a 3D Printed Wind Tunnel Model with Integrated Force Measurement
Wind tunnel testing using conventionally manufactured force measurement systems (balances) is able to yield precise and accurate results. These balances are delicate, complex instruments. Due to the nature of this high-precision complexity, balances are expensive and time-consuming to produce. These factors can limit the design potential of the model because using conventionally manufactured balances generally means that the model is designed to accommodate balances that already exist. Ideally, engineers would be able to design a measurement system that is tailored to fit the needs of the model, rather than the other way around. The goal of this research project was to determine if it is possible to overcome these drawbacks by designing a wind tunnel model with a modifiable integrated force measurement system that leverages additive manufacturing. This was undertaken by utilizing the existing UT-63 wind tunnel balance (measurement system), integrating it with a model based on the existing AGARD-B model, and performing stress analysis to optimize the design. Due to time constraints, the result of this endeavor is currently incomplete. Continued analysis and optimization are required to have a thorough understanding of the success or failure of the attempted design. As additive manufacturing technology evolves, the ability for it to become the primary method of fabrication for sensitive measurement systems increases. This project contributes to this by providing one method for design, a framework for optimizing existing balances for additive manufacturing, and resources for performing necessary stress analysis.
Callie Ellsworth↗
Ontological Modeling for Integrated Spacecraft Analysis
Current spacecraft work as a cooperative group of a number of subsystems. Each of these requiresmodeling software for development, testing, and prediction. It is the goal of my team to create anoverarching software architecture called the Integrated Spacecraft Analysis (ISCA) to aid in deploying the discrete subsystems' models. Such a plan has been attempted in the past, and has failed due to the excessive scope of the project. Our goal in this version of ISCA is to use new resources to reduce the scope of the project, including using ontological models to help link the internal interfaces of subsystems' models with the ISCA architecture.I have created an ontology of functions specific to the modeling system of the navigation system of a spacecraft. The resulting ontology not only links, at an architectural level, language specificinstantiations of the modeling system's code, but also is web-viewable and can act as a documentation standard. This ontology is proof of the concept that ontological modeling can aid in the integration necessary for ISCA to work, and can act as the prototype for future ISCA ontologies.
ontological modeling↗
Machine learning-enabled model-data integration for predicting subsurface water storage
Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change.
Lu, Dan↗
Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]
The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Diagnosis by integrating model-based reasoning with knowledge-based reasoning
Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.
Bylander, Tom↗
Deep Impact Sequence Planning Using Multi-Mission Adaptable Planning Tools With Integrated Spacecraft Models
The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.
Deep Impact Mission↗
Deep impact sequence planning using multi-mission adaptable planning tools with integrated spacecraft models
The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.
Seybold, Calina C.↗
PyHydroGeophysX: An extensible open-source platform for integrating hydrological models with geophysical measurements
Hydrological models and geophysical measurements are widely used tools for understanding subsurface hydrological processes relevant to water resource management, yet they typically remain disconnected due to technical barriers. We present PyHydroGeophysX, an open-source Python platform bridging this gap by providing standardized interfaces between hydrological modeling software (MODFLOW, ParFlow) and geophysical simulation tools (PyGIMLi, SimPEG). The platform implements bidirectional workflows: translating hydrological outputs into simulated geophysical responses through petrophysical models, and extracting hydrological information from geophysical inversions. Key features include bidirectional workflow modules, configurable petrophysical models, time-lapse inversion with temporal regularization, parallel computing, and mesh utilities for property transfer between geophysical and hydrological grids. The modular architecture of PyHydroGeophysX enables researchers to incorporate additional models and methods, fostering broader adoption of integrated hydrogeophysical approaches. The software is freely available on GitHub and is intended for researchers and practitioners working at the intersection of hydrology and geophysics.
Hydrogeophysics↗
Cell output, cell cycle duration and neuronal specification: a model of integrated mechanisms of the neocortical proliferative process
The neurons of the neocortex are generated over a 6 day neuronogenetic interval that comprises 11 cell cycles. During these 11 cell cycles, the length of cell cycle increases and the proportion of cells that exits (Q) versus re-enters (P) the cell cycle changes systematically. At the same time, the fate of the neurons produced at each of the 11 cell cycles appears to be specified at least in terms of their laminar destination. As a first step towards determining the causal interrelationships of the proliferative process with the process of laminar specification, we present a two-pronged approach. This consists of (i) a mathematical model that integrates the output of the proliferative process with the laminar fate of the output and predicts the effects of induced changes in Q and P during the neuronogenetic interval on the developing and mature cortex and (ii) an experimental system that allows the manipulation of Q and P in vivo. Here we show that the predictions of the model and the results of the experiments agree. The results indicate that events affecting the output of the proliferative population affect both the number of neurons produced and their specification with regard to their laminar fate.
Evaluation Studies↗
Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations
The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.
54 ENVIRONMENTAL SCIENCES↗
Predictive Integrated Stratigraphic Modeling Report, Center-wide Per- and Polyfluoroalkyl Substances, Potential Release Location 237, Kennedy Space Center, FL
The National Aeronautics and Space Administration (NASA) is investigating the occurrence of per- and polyfluoroalkyl substances (PFAS) Center-wide at Kennedy Space Center (KSC), Merritt Island, Florida (Figure 1-1) as Potential Release Location 237. AECOM Technical Services, Inc. (AECOM) conducted investigation activities in 2020 and 2021 to develop a better understanding of the fate and transport of PFAS in groundwater and surface water at KSC center-wide using PRedictive Integrated Stratigraphic Modeling (PRISM). PRISM uses best practices from the fields of geology, hydrology, and chemistry to acquire a holistic understanding of the subsurface and more accurately predict contaminant migration pathways. Based on the potential magnitude (frequency and areal distribution) of PFAS impacts at KSC, the analyses described herein were designed to provide tools to address the unique behavior of PFAS chemicals.
PFAS↗
Industry Level Integrated Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)
The software Fire Risk Investigation in 3D (FRI3D) has been developed over the last 2 years to integrate 3D spatial modeling with existing fire probabilistic risk assessment (PRA) models and fire simulation codes. The goal of this research and development is to automate many of the fire analysis manual tasks to reduce industry efforts in the initial fire modeling and operational costs for the model maintenance and evaluations required during normal plant operations. The tasks for Fiscal Year (FY) 2021 include first testing the FRI3D modeling capabilities by importing an industry fire model into FRI3D and making a 3D model of a complex/high-risk significant area. (For this work, the switchgear room was chosen.) Then, the second task of FY 2021 is to develop a dynamic fire PRA process that can help optimize traditional fire PRA models. The switchgear room model will be used for the dynamic fire PRA work. This report describes the work and insights learned when using FRI3D software to model both a Nuclear Regulatory Report (NUREG) example models and a full industry switchgear room.
97 MATHEMATICS AND COMPUTING↗
Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment
Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.
Probability Theory↗
Integrating O/S models during conceptual design, part 1
The University of Dayton is pleased to submit this report to the National Aeronautics and Space Administration (NASA), Langley Research Center, which integrates a set of models for determining operational capabilities and support requirements during the conceptual design of proposed space systems. This research provides for the integration of the reliability and maintainability (R&M) model, both new and existing simulation models, and existing operations and support (O&S) costing equations in arriving at a complete analysis methodology. Details concerning the R&M model and the O&S costing model may be found in previous reports accomplished under this grant (NASA Research Grant NAG1-1327). In the process of developing this comprehensive analysis approach, significant enhancements were made to the R&M model, updates to the O&S costing model were accomplished, and a new simulation model developed. This is the 1st part of a 3 part technical report.
Ebeling, Charles E.↗
Quantifying co-benefits of water quality policies: An integrated assessment model of land and nitrogen management
Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co-benefits.” To quantify the co-benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri-environmental system and offers a direction for future studies. Our model results highlight the finding that the co-benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co-benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.
54 ENVIRONMENTAL SCIENCES↗