Results for “Reliability Prediction” — Page 45

NASA NTRSDate not supplied

Gas-Particle Interaction Model Development in Plume Surface Interaction Erosion and Cratering

As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The GCD PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC) Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper describes the activities and accomplishments of the past year in the gas-particle interaction modeling portion of the GCD PSI project. The objectives of this task are to investigate and construct models of particle drag and gas-particle cloud interactions leading to what is known as particle turbulent kinetic energy (PTKE). While drag on a lone particle suspended in a flow gas of gas is well-known, the drag and drag-induced dynamics of a cloud of particles in a gas flow are not. The PSC element of the PSI project requires models of gas-particle cloud interactions to implement in the Gas Granular Flow Solver (GGFS) in order to arrive at a predictive simulation capability for PSI-induced soil erosion, cratering and ejecta transport. Experiments of dilute mixtures of soil particles in supersonic gas flow are being conducted at Johns Hopkins University in order to provide a dataset describing gas-particle cloud interactions. Augmented by a separate funding source are efforts to perform small-scale particle-resolved direct numerical simulations (PR-DNS) and larger scale Eulerian-Lagrangian simulations. Together, these experiments and simulations are being used to devise models of particle cloud drag and particle cloud drag-induced dynamics. The final product of these efforts will be particle drag models suitable for implementation into the GGFS application and a PTKE model suitable for the same purpose.

Jeff West↗

NASA NTRSDate not supplied

Overview of the Predictive Simulation Capability Element of the Plume Surface Interaction Project

As part of the Game Changing Development (GCD) Program funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial plume surface interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The GCD PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC)Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element.

Plume Surface Interaction↗

NASA NTRSDate not supplied

Qualitative Comparison of Pathfinder Three-Dimensional Transient Gas Granular Simulation Results to Imagery from a Subscale, Reduced Pressure Plume Surface Interaction Ground Test

NASA’s Game Changing Development Program, funded by NASA’s Space Technology Mission Directorate, the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC) Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~600 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the PSI Project also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper describes the first Pathfinder three-dimensional, transient simulation of the test configuration used to acquire erosion and cratering test data as part of the Physics Focused Ground Test conducted by the PSI Project in FY2021. This first pathfinder simulation was conducted using the Loci/GGFS (Gas Granular Flow Solver), a computational fluid dynamics tool developed in part with funding provided by the PSI Project. Crater growth and ejecta flow paths from the pathfinder simulation is compared to raw high-speed imagery PFGT results of the crater growth. There is significant similarity of test and simulation to warrant expending resources on a more rigorous quantitative Validation Assessment.

J S West↗

NASA NTRSDate not supplied

Qualitative Comparison of Pathfinder Three-Dimensional Transient Gas Granular Simulation Results to Imagery from a Subscale, Reduced Pressure Plume Surface Interaction Ground Test

NASA’s Game Changing Development Program, funded by NASA’s Space Technology Mission Directorate, the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC) Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~600 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the PSI Project also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper describes the first Pathfinder three-dimensional, transient simulation of the test configuration used to acquire erosion and cratering test data as part of the Physics Focused Ground Test conducted by the PSI Project in FY2021. This first pathfinder simulation was conducted using the Loci/GGFS (Gas Granular Flow Solver), a computational fluid dynamics tool developed in part with funding provided by the PSI Project. Crater growth and ejecta flow paths from the pathfinder simulation is compared to raw high-speed imagery PFGT results of the crater growth. There is significant similarity of test and simulation to warrant expending resources on a more rigorous quantitative Validation Assessment.

Jeff West↗

NASA NTRSDate not supplied

Four Problematic Methods in Reliability Analysis

Some basic methods used in reliability analysis are problematic because they produce incorrect and overoptimistic predictions. Initially gratifying forecasts are often invalidated by testing and operational experience. The problematic methods in reliability analysis include estimating the system failure rate as the sum of component failure rates, assuming that reliability growth continues indefinitely during testing, overestimating the benefits of redundancy, and using the fault tolerance count instead of a detailed reliability analysis. Reliability analysis can produce more optimism than accuracy. This bug may now be a feature. The optimistic bias inevitable in project planning should be corrected by realistic reliability analysis that reflects relevant experience. That the repeated poor performance of reliability analysis is found to be surprising suggests willful blindness. Rigorous methods and impartial critical review are necessary to improve reliability analysis.

Reliability analysis↗

NASA NTRSDate not supplied

Four Problematic Methods in Reliability Analysis

Some basic methods used in reliability analysis are problematic because they produce incorrect and overoptimistic predictions. Initially gratifying forecasts are often invalidated by testing and operational experience. The problematic methods in reliability analysis include estimating the system failure rate as the sum of component failure rates, assuming that reliability growth continues indefinitely during testing, overestimating the benefits of redundancy, and using the fault tolerance count instead of a detailed reliability analysis. Reliability analysis can produce more optimism than accuracy. This bug may now be a feature. The optimistic bias inevitable in project planning should be corrected by realistic reliability analysis that reflects relevant experience. That the repeated poor performance of reliability analysis is found to be surprising suggests willful blindness. Rigorous methods and impartial critical review are necessary to improve reliability analysis.

Reliability analysis↗

DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Overview of Predictive Simulation Capability Development for Crater Evolution and Ejecta in Continuum/Rarefied Flows

As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The Predictive Simulation Capability (PSC) Element is focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the predictive simulation capability, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. The focus of this paper is to provide an overview and a brief status on one area of capability development through this GCD PSI project - capturing the effects of mixed continuum/rarefied flow on crater development and ejecta sheets. This includes both capability enhancements and maturation of the predictive tool, Loci/GGFS, into a production capable tool as well as validation of the tool against PSI data in Martian and Lunar environments. There are six newly developed capabilities that will be production ready by the end of the fiscal year. Newly developed capabilities must first be verified prior to being production ready, and this process includes capability demonstration, solution verification, and performance evaluation. An overview of the newly developed capabilities with respect to several test cases, including cratering simulations of the Apollo Lunar Module, will be presented. Several validation analyses have been performed to validate in Martian and more Lunar-like environments, including simulations of cratering from a subsonic flow test at Planetary Aeolian Lab (PAL) conducted in a Martian-like environment and pre-test cratering predictions of Physics Focused Ground Test (PFGT) tests planned to be performed in Martian-like and more Lunar-like environments. An overview of these validation analyses as it pertains to the capability development will be presented.

Andrew Weaver↗

NASA NTRSDate not supplied

Particle Interaction Physics Model Formulation for Plume-Surface Interaction Erosion and Cratering

As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The Predictive Simulation Capability (PSC) Element is focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the predictive simulation capability, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper will present the status of implementing and maturing particle-particle interaction constituent physics models essential in simulating the landing surface granular material flow under PSI effects. This gas-particle multi-phase interaction modeling of plume impingement flow on the extra-terrestrial soil material is performed with the Gas-Granular Flow Solver (GGFS) addressed in a companion paper. The response of regolith particle flow induced by lander PSI requires accurate representation of the regolith granular material fluidic behavior and gas-granular interactions. The lunar regolith, as the extreme example, is poorly sorted with broad particle size distributions and large fines content. It has significant cohesion, due to interlocking particle shapes for the very jagged particles. The combination of particle shape and size distribution has been identified as major drivers in the complex particle flow response and resulting crater shape characteristics of extraterrestrial granular material. Constituent models for spherical particles can be formulated directly from particle kinetics theory. Complex particle shapes can be modeled by gluing together elemental spherical shapes into composite particles, requiring a Discrete Element Model (DEM) particle kinetics modeling approach to extract data and formulate constituent models. Mixture constituent models for poly-disperse mixtures (i.e, containing distribution of particle sizes) have recently been developed. The required non-spherical particle mixture granular material response closure models are then obtained through small-scale unit physics DEM simulations for the range of particle shapes, mixtures and packing densities. The granular material response closure models are then implemented in the Eulerian granular flow formulation. This DEM-based constituent model extraction process and formulation of poly-disperse particle mixtures has been successfully developed by small business and academic partners in the development of the Gas-Granular Flow Solver (GGFS) simulation program simulation framework. The currently implemented capabilities have reached the capability level of modeling bi-disperse, non-spherical particle mixtures is being continuously extended towards computational modeling of full range irregular particle mixtures. Under the GCD project, this technology is being further developed, transferred to NASA analysts, and matured towards application readiness. The predictive simulation capability team under the GCD project has acquired the modeling tools and processes of the DEM based constituent model formulation from the GGFS development team and is developing the capability to replicate the existing process. This is the first important step towards the ability of the NASA team to independently perform such model development in a production setting. Further efforts are underway to migrate the DEM based model simulation process performed with the academic based tools to more capable Open Source, highly parallelized simulation tools for efficient operation on NASA HPC assets. Evaluation of the currently implemented (such as mono-disperse and bi-disperse spherical and irregular shape particle constituent model applications) and continuously evolving full-range particle physics models in the GGFS tool is performed by the NASA team to advance application readiness of the simulations. Application testing for complex PSI erosions and cratering scenarios such as the Apollo LM is performed for axi-symmetric and full 3D simulations to aid the tool developers in achieving practical application readiness for NASA projects. Important validation and application testing will further be performed against experimental data generated under the GCD PSI project experimental component.

Peter A Liever↗

NASA NTRSDate not supplied

Overview of Predictive Simulation Capability Development for Crater Evolution and Ejecta in Continuum/Rarefied Flows

As part of the Game Changing Development (GCD) Program funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial plume surface interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The Predictive Simulation Capability (PSC) Element is focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In this paper, an overview and brief status on one area of capability development through this GCD PSI project, capturing the effects of mixed continuum/rarefied flow on crater development and ejecta sheets, is presented with respect to capability enhancements, predictive tool maturation, and validation of the Loci/GGFS tool against PSI data in Martian and Lunar environments. Axisymmetric modeling, overset mesh/moving mesh, and multiple-species mixture modeling capabilities are verified against Loci/CHEM, and a demonstration of the capabilities is presented for the Apollo Lunar Module.

Andrew Weaver↗

DOE OSTI2022

Physically Informed Machine Learning Prediction of Electronic Density of States

The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. Still, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Limiting Current in Nanostructured Block Copolymer Electrolytes

The next-generation electrolytes for lithium batteries must be able to conduct ions at sufficiently high current densities; yet this regime is rarely studied directly. The limiting current density of an electrolyte quantifies the highest possible rate of ion transport under an applied dc potential. Herein, we report on the limiting current density in twelve nanostructured polystyrene-block-poly(ethylene oxide) (PS-b-PEO, or SEO) copolymer electrolytes. We find that the limiting current at a given salt concentration increases systematically with increasing volume fraction of the PEO block (φ EO ). In contrast, the effective-medium theory, commonly used to analyze conductivity in block copolymer electrolytes, predicts that limiting current is independent of φ EO . To resolve this conundrum, the ionic conductivity, the mutual diffusion coefficient of the salt, and the steady-state current fraction of the block copolymer electrolytes were measured. These measurements enable predictions of limiting current with no adjustable parameters using the concentrated solution theory. We found quantitative agreement between experimentally measured limiting current densities and predictions based on the concentrated solution theory. This work sheds light on how to reliably measure and predict limiting current density in composite electrolytes.

25 ENERGY STORAGE↗

NASA NTRS1989

Software reliability studies

The longterm goal of this research is to identify or create a model for use in analyzing the reliability of flight control software. The immediate tasks addressed are the creation of data useful to the study of software reliability and production of results pertinent to software reliability through the analysis of existing reliability models and data. The completed data creation portion of this research consists of a Generic Checkout System (GCS) design document created in cooperation with NASA and Research Triangle Institute (RTI) experimenters. This will lead to design and code reviews with the resulting product being one of the versions used in the Terminal Descent Experiment being conducted by the Systems Validations Methods Branch (SVMB) of NASA/Langley. An appended paper details an investigation of the Jelinski-Moranda and Geometric models for software reliability. The models were given data from a process that they have correctly simulated and asked to make predictions about the reliability of that process. It was found that either model will usually fail to make good predictions. These problems were attributed to randomness in the data and replication of data was recommended.

Wilson, Larry W.↗

NASA NTRS1991

Reliability analysis of structural ceramics subjected to biaxial flexure

The reliability of alumina disks subjected to biaxial flexure is predicted on the basis of statistical fracture theory using a critical strain energy release rate fracture criterion. Results on a sintered silicon nitride are consistent with reliability predictions based on pore-initiated penny-shaped cracks with preferred orientation normal to the maximum principal stress. Assumptions with regard to flaw types and their orientations in each ceramic can be justified by fractography. It is shown that there are no universal guidelines for selecting fracture criteria or assuming flaw orientations in reliability analyses.

Chao, Luen-Yuan↗

NASA NTRS1985

A performance and reliability model for thermal barrier coatings

A modeling technique for predicting the performance and reliability of TBC's is being developed at Solar Turbines Incorporated. The concept combines experimental coating property data with finite element analyses to predict the thermal and mechanical behavior of coating systems in service. A key feature of Solar's approach is the use of a four point flexure test to estimate coating strength distributions and to predict coating failure probability. This model was used to evaluate the effect of physical variations on coating performance in high heat flux rocket engine applications for NASA. Current work, promoted by Caterpillar Tractor Company for diesel engine applications, is being conducted to measure coating strength as a function of temperature, and future work will document strength degradation with time at temperature. Solar's interest lies in the application of TBCs to gas turbine engine components.

Batakis, A. P.↗

DOE OSTI2021

HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization

We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete.

adaptation models↗

NASA NTRS1992

An overview of reliability growth models and their potential use for NASA applications

An overview is provided of reliability growth literature over the past 25 years. This includes a thorough literature review of different areas of the application of reliability growth such as design, prediction, tracking/management, and demonstration. Various reliability growth models use different bases on how they characterize growth. Different models are discussed. Also, the use is addressed of reliability growth models to NASA applications. This includes the application of these models to the space shuttle main engine. For potential NASA applications, we classify growth models in two groups, which are characterized.

Taneja, V. S.↗

NASA NTRS2015

Reliability and Probabilistic Risk Assessment - How They Play Together

PRA methodology is one of the probabilistic analysis methods that NASA brought from the nuclear industry to assess the risk of LOM, LOV and LOC for launch vehicles. PRA is a system scenario based risk assessment that uses a combination of fault trees, event trees, event sequence diagrams, and probability and statistical data to analyze the risk of a system, a process, or an activity. It is a process designed to answer three basic questions: What can go wrong? How likely is it? What is the severity of the degradation? Since 1986, NASA, along with industry partners, has conducted a number of PRA studies to predict the overall launch vehicles risks. Planning Research Corporation conducted the first of these studies in 1988. In 1995, Science Applications International Corporation (SAIC) conducted a comprehensive PRA study. In July 1996, NASA conducted a two-year study (October 1996 - September 1998) to develop a model that provided the overall Space Shuttle risk and estimates of risk changes due to proposed Space Shuttle upgrades. After the Columbia accident, NASA conducted a PRA on the Shuttle External Tank (ET) foam. This study was the most focused and extensive risk assessment that NASA has conducted in recent years. It used a dynamic, physics-based, integrated system analysis approach to understand the integrated system risk due to ET foam loss in flight. Most recently, a PRA for Ares I launch vehicle has been performed in support of the Constellation program. Reliability, on the other hand, addresses the loss of functions. In a broader sense, reliability engineering is a discipline that involves the application of engineering principles to the design and processing of products, both hardware and software, for meeting product reliability requirements or goals. It is a very broad design-support discipline. It has important interfaces with many other engineering disciplines. Reliability as a figure of merit (i.e. the metric) is the probability that an item will perform its intended function(s) for a specified mission profile. In general, the reliability metric can be calculated through the analyses using reliability demonstration and reliability prediction methodologies. Reliability analysis is very critical for understanding component failure mechanisms and in identifying reliability critical design and process drivers. The following sections discuss the PRA process and reliability engineering in detail and provide an application where reliability analysis and PRA were jointly used in a complementary manner to support a Space Shuttle flight risk assessment.

Safie, Fayssal M.↗