Search NASA⌕ Search

SEARCH · Search NASA

Results for “probabilistic modeling”

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.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Logistics Modeling for Lunar Exploration Systems

The extensive logistics required to support extended crewed operations in space make effective modeling of logistics requirements and deployment critical to predicting the behavior of human lunar exploration systems. This paper discusses the software that has been developed as part of the Campaign Manifest Analysis Tool in support of strategic analysis activities under the Constellation Architecture Team - Lunar. The described logistics module enables definition of logistics requirements across multiple surface locations and allows for the transfer of logistics between those locations. A key feature of the module is the loading algorithm that is used to efficiently load logistics by type into carriers and then onto landers. Attention is given to the capabilities and limitations of this loading algorithm, particularly with regard to surface transfers. These capabilities are described within the context of the object-oriented software implementation, with details provided on the applicability of using this approach to model other human exploration scenarios. Some challenges of incorporating probabilistics into this type of logistics analysis model are discussed at a high level.

Andraschko, Mark R.↗
NASA NTRS2003

Application of Probabilistic Analysis to Aircraft Impact Dynamics

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stressstrain behaviors, laminated composites, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the uncertainty in the simulated responses. Several criteria are used to determine that a response surface method is the most appropriate probabilistic approach. The work is extended to compare optimization results with and without probabilistic constraints.

Lyle, Karen H.↗
DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS1976

Reliability and coverage analysis of non-repairable fault-tolerant memory systems

A method was developed for the construction of probabilistic state-space models for nonrepairable systems. Models were developed for several systems which achieved reliability improvement by means of error-coding, modularized sparing, massive replication and other fault-tolerant techniques. From the models developed, sets of reliability and coverage equations for the systems were developed. Comparative analyses of the systems were performed using these equation sets. In addition, the effects of varying subunit reliabilities on system reliability and coverage were described. The results of these analyses indicated that a significant gain in system reliability may be achieved by use of combinations of modularized sparing, error coding, and software error control. For sufficiently reliable system subunits, this gain may far exceed the reliability gain achieved by use of massive replication techniques, yet result in a considerable saving in system cost.

Cox, G. W.↗
NASA NTRS1992

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Boyce, Lola↗
NASA NTRS1992

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep, and thermal fatigue

The results of a fourth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA) are presented. The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue, or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation was randomized and is included in the computer program, PROMISC. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Boyce, Lola↗
NASA NTRS1992

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep and thermal fatigue

This report presents the results of a fourth year effort of a research program, conducted for NASA-LeRC by the University of Texas at San Antonio (UTSA). The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subject to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 has been analyzed using the developed methodology.

Boyce, Lola↗
NASA NTRS1990

EDNA: Expert fault digraph analysis using CLIPS

Traditionally fault models are represented by trees. Recently, digraph models have been proposed (Sack). Digraph models closely imitate the real system dependencies and hence are easy to develop, validate and maintain. However, they can also contain directed cycles and analysis algorithms are hard to find. Available algorithms tend to be complicated and slow. On the other hand, the tree analysis (VGRH, Tayl) is well understood and rooted in vast research effort and analytical techniques. The tree analysis algorithms are sophisticated and orders of magnitude faster. Transformation of a digraph (cyclic) into trees (CLP, LP) is a viable approach to blend the advantages of the representations. Neither the digraphs nor the trees provide the ability to handle heuristic knowledge. An expert system, to capture the engineering knowledge, is essential. We propose an approach here, namely, expert network analysis. We combine the digraph representation and tree algorithms. The models are augmented by probabilistic and heuristic knowledge. CLIPS, an expert system shell from NASA-JSC will be used to develop a tool. The technique provides the ability to handle probabilities and heuristic knowledge. Mixed analysis, some nodes with probabilities, is possible. The tool provides graphics interface for input, query, and update. With the combined approach it is expected to be a valuable tool in the design process as well in the capture of final design knowledge.

Dixit, Vishweshwar V.↗
NASA NTRS2003

Satellite Detection of the Convection Generated Stresses in Earth

We review research developments on satellite detection of the convection generated stresses in the Earth for seismic hazard assessment and Earth resource survey. Particular emphasis is laid upon recent progress and results of stress calculations from which the origin and evolution of the tectonic features on Earth's surface can be scientifically addressed. An important aspect of the recent research development in tectonic stresses relative to earthquakes is the implications for earthquake forecasting and prediction. We have demonstrated that earthquakes occur on the ring of fire around the Pacific in response to the tectonic stresses induced by mantle convection. We propose a systematic global assessment of the seismic hazard based on variations of tectonic stresses in the Earth as observed by satellites. This space geodynamic approach for assessing the seismic hazard is unique in that it can pinpoint the triggering stresses for large earthquakes without ambiguities of geological structures, fault geometries, and other tectonic properties. Also, it is distinct from the probabilistic seismic hazard assessment models in the literature, which are based only on extrapolations of available earthquake data.

Liu, Han-Shou↗
NASA NTRS2011

Small Orbital Debris Mitigation Mission Architecture

Small orbital debris in LEO (1-10 cm in size) presents a clear and present danger to operational LEO spacecraft. This debris field has dramatically increased (nearly doubled) in recent years following the Chinese ASAT Test in 2007 and the Iridium/Cosmos collision in 2009. Estimates of the number of small debris have grown to 500,000 objects after these two events; previously the population was 300,000 objects. These small, untracked debris objects (appproximately 500,000) outnumber the larger and tracked objects (appproximately 20,000) by a factor 25 to 1. Therefore, the risk of the small untracked debris objects to operational spacecraft is much greater than the risk posed by the larger and tracked LEO debris objects. A recent study by The Aerospace Corporation found that the debris environment will increase the costs of maintaining a constellation of government satellites by 5%, a constellation of large commercial satellites by 11%, and a constellation of factory built satellites by 26% from $7.6 billion to $9.57 billion. Based upon these facts, the NASA Marshall Space Flight Center (MSFC) Advanced Concepts Office (ACO) performed an architecture study on Small Orbital Debris Active Removal (SODAR) using a space-based nonweapons- class laser satellite for LEO debris removal. The goal of the SODAR study was to determine the ability of a space-based laser system to remove the most pieces of debris (1 cm to 10 cm, locations unknown), in the shortest amount of time, with the fewest number of spacecraft. The ESA developed MASTER2005 orbital debris model was used to probabilistically classify the future debris environment including impact velocity, magnitude, and directionality. The study ground rules and assumptions placed the spacecraft into a high inclination Low Earth Orbit at 800 km as an initial reference point. The architecture study results found that a spacecraft with an integrated forward-firing laser is capable of reducing the small orbital debris flux within a 60 to 100 km orbital shell by a significant amount within the one spacecraft's operational lifetime. The technology developments required for such an architecture to be successfully employed are: 1) A pico-pulsed, space qualified laser and 2) The ability to detect and track a small LEO small debris object from a space based platform. Therefore, a conceptual design for a demonstration satellite to showcase the ability to detect and track small orbital debris was completed

Wiegmann, Bruce M.↗
NASA NTRS2013

PRA and Conceptual Design

Once a project obtains approval, decision makers have to consider a variety of alternative paths for completing the project and meeting the project objectives. How decisions are made involves a variety of elements including: cost, experience, current technology, ideologies, politics, future needs and desires, capabilities, manpower, timing, available information, and for many ventures management needs to assess the elements of risk versus reward. The use of high level Probabilistic Risk Assessment (PRA) Models during conceptual design phases provides management with additional information during the decision making process regarding the risk potential for proposed operations and design prototypes. The methodology can be used as a tool to: 1) allow trade studies to compare alternatives based on risk, 2) determine which elements (equipment, process or operational parameters) drives the risk, and 3) provide information to mitigate or eliminate risks early in the conceptual design to lower costs. Creating system models using conceptual design proposals and generic key systems based on what is known today can provide an understanding of the magnitudes of proposed systems and operational risks and facilitates trade study comparisons early in the decision making process. Identifying the "best" way to achieve the desired results is difficult, and generally occurs based on limited information. PRA provides a tool for decision makers to explore how some decisions will affect risk before the project is committed to that path, which can ultimately save time and money.

DeMott, Diana↗
NASA NTRS2014

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

The theory of System Health Management (SHM) and of its operational subset Fault Management (FM) states that FM is implemented as a "meta" control loop, known as an FM Control Loop (FMCL). The FMCL detects that all or part of a system is now failed, or in the future will fail (that is, cannot be controlled within acceptable limits to achieve its objectives), and takes a control action (a response) to return the system to a controllable state. In terms of control theory, the effectiveness of each FMCL is estimated based on its ability to correctly estimate the system state, and on the speed of its response to the current or impending failure effects. This paper describes how this theory has been successfully applied on the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program to quantitatively estimate the effectiveness of proposed abort triggers so as to select the most effective suite to protect the astronauts from catastrophic failure of the SLS. The premise behind this process is to be able to quantitatively provide the value versus risk trade‐off for any given abort trigger, allowing decision makers to make more informed decisions. All current and planned crewed launch vehicles have some form of vehicle health management system integrated with an emergency launch abort system to ensure crew safety. While the design can vary, the underlying principle is the same: detect imminent catastrophic vehicle failure, initiate launch abort, and extract the crew to safety. Abort triggers are the detection mechanisms that identify that a catastrophic launch vehicle failure is occurring or is imminent and cause the initiation of a notification to the crew vehicle that the escape system must be activated. While ensuring that the abort triggers provide this function, designers must also ensure that the abort triggers do not signal that a catastrophic failure is imminent when in fact the launch vehicle can successfully achieve orbit. That is, the abort triggers must have low false negative rates to be sure that real crew‐threatening failures are detected, and also low false positive rates to ensure that the crew does not abort from non‐crew‐threatening launch vehicle behaviors. The analysis process described in this paper is a compilation of over six years of lessons learned and refinements from experiences developing abort triggers for NASA's Constellation Program (Ares I Project) and the SLS Program, as well as the simultaneous development of SHM/FM theory. The paper will describe the abort analysis concepts and process, developed in conjunction with SLS Safety and Mission Assurance (S&MA) to define a common set of mission phase, failure scenario, and Loss of Mission Environment (LOME) combinations upon which the SLS Loss of Mission (LOM) Probabilistic Risk Assessment (PRA) models are built. This abort analysis also requires strong coordination with the Multi‐Purpose Crew Vehicle (MPCV) and SLS Structures and Environments (STE) to formulate a series of abortability tables that encapsulate explosion dynamics over the ascent mission phase. The design and assessment of abort conditions and triggers to estimate their Loss of Crew (LOC) Benefits also requires in‐depth integration with other groups, including Avionics, Guidance, Navigation and Control(GN&C), the Crew Office, Mission Operations, and Ground Systems. The outputs of this analysis are a critical input to SLS S&MA's LOC PRA models. The process described here may well be the first full quantitative application of SHM/FM theory to the selection of a sensor suite for any aerospace system.

Lo, Yunnhon↗
NASA NTRS2011

Reliability Analysis and Standardization of Spacecraft Command Generation Processes

center dot In order to reduce commanding errors that are caused by humans, we create an approach and corresponding artifacts for standardizing the command generation process and conducting risk management during the design and assurance of such processes. center dot The literature review conducted during the standardization process revealed that very few atomic level human activities are associated with even a broad set of missions. center dot Applicable human reliability metrics for performing these atomic level tasks are available. center dot The process for building a "Periodic Table" of Command and Control Functions as well as Probabilistic Risk Assessment (PRA) models is demonstrated. center dot The PRA models are executed using data from human reliability data banks. center dot The Periodic Table is related to the PRA models via Fault Links.

human errors.↗
NASA NTRS2018

Bridging the Engineering and Medicine Gap

A primary challenge NASA faces is communication between the disparate entities of engineers and human system experts in life sciences. Clear communication is critical for exploration mission success from the perspective of both risk analysis and data handling. The engineering community uses probabilistic risk assessment (PRA) models to inform their own risk analysis and has extensive experience managing mission data, but does not always fully consider human systems integration (HSI). The medical community, as a part of HSI, has been working 1) to develop a suite of tools to express medical risk in quantitative terms that are relatable to the engineering approaches commonly in use, and 2) to manage and integrate HSI data with engineering data. This talk will review the development of the Integrated Medical Model as an early attempt to bridge the communication gap between the medical and engineering communities in the language of PRA. This will also address data communication between the two entities in the context of data management considerations of the Medical Data Architecture. Lessons learned from these processes will help identify important elements to consider in future communication and integration of these two groups.

Walton, M.↗
NASA NTRS2018

The Growth of Mass and Reliability Source Database and Me

Mass and Reliability Source (MaRS) is a database which draws together information from multiple other sources and databases (MADS, VMDB, ISS PART, Bayesian PowerPoint, etc.). Information such as mass, operating hours, and failure data are compiled to create a tool for the building of probabilistic Risk Assessment (PRA) models. MaRS also has great potential for deep space mission planning. Understanding the lowest point of failure within an ORU helps to inform the efficient selection of spare parts necessary for a journey where mass and volume are at a premium and resupply is not possible.

George, Cory A.↗
NASA NTRS2020

Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances

Current methods to retrieve optically relevant properties from ocean color observations do not explicitly make use of prior knowledge about property distributions. Here we implement a simplified Bayesian approach that takes into account prior probability distributions on two sets of five optically relevant parameters, and conduct a retrieval of these parameters using hyperspectral simulated water-leaving reflectances. We focus specifically on the ability of the model to distinguish between two optically similar phytoplankton taxa, diatoms and Noctiluca scintillans. The inversion retrieval gives most-likely concentrations and uncertainty estimates, and we find that the model is able to probabilistically predict the occurrence of Noctiluca scintillans blooms using these metrics. We discuss how this method can be expanded to include a priori covariances between different parameters, and show the effect of varying measurement uncertainty and spectral resolution on Noctiluca scintillans bloom predictions.

Zachary K Erickson↗
NASA NTRSDate not supplied

Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables

The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.

A. Anderson↗